Query Understanding: A Manifesto

•

23 recomendaciones•17,084 vistas

Query understanding is about focusing less on the results and more on the query. It’s about figuring out what the searcher wants, rather than scoring and ranking results. Once you’ve established this mindset, your approach to search changes: you focus on query performance rather than ranking. Presented at QConSF 2016: https://qconsf.com/sf2016/presentation/query-understanding-manifesto

Software

Query Understanding:
A Manifesto
Daniel Tunkelang
queryunderstanding.com

Overview
• What is query understanding?
• Query performance prediction.
• Query rewriting.
• Query suggestions.
• Search is a conversation.
tl;dr: Query understanding is about focusing 
less on the results and more on the query.

Query understanding means
ﬁguring out what the searcher wants.

What query understanding is not:
scoring and ranking of results.

Query understanding means not relying 
on ranking to ﬁlter out irrelevant results.

Challenge: accurately identify searcher's intent 
while minimizing searcher’s effort.

Old-school search: 
searcher enters query, search engine interprets query.

Modern search: 
searcher starts to type, search engine suggests queries.

And in 2016,
modern search means mobile search.

March Hare: “You should say what you mean.” 
Alice: “I do. At least I mean what I say.”

But what if the search engine doesn’t 
know what the searcher means?

And how does a search engine achieve the 
self-awareness to know what it doesn’t know?

Query understanding is about measuring 
and optimizing for query performance.

History is the best teacher.
For common queries, learn from historical performance.

How you deﬁne performance depends on why
searchers use your site. But deﬁne it and measure it!

Can also learn from similar queries:
re-ordered terms, subqueries, similar results, etc.

Stuck in the long tail? 
Predict performance from query and result set features.

Lots of academic work on query performance prediction.

So you have a poorly performing query. Now what?
Rewrite it!

Rewriting to improve precision:
query segmentation, ﬁeld restriction, query classiﬁcation.
tag: skill OR title
related skills:
search, ranking, …
tag: company
id: 1337
industry: internet
verticals:
people, jobs
intent: exploratory

Rewriting to improve recall:
query expansion, query relaxation.

When query expansion is done right, users barely notice.
Query relaxation is risker and should be messaged.

Query rewriting should improve query performance.
If it doesn’t, you’re doing wrong. Measure!

Rewriting is great when searchers type whole queries.
But what about autocomplete and search suggestions?

Query suggestions have two jobs:
reduce searcher effort and improve query performance.

Searchers are people. People are lazy.
All else equal, help them type as little as possible.

But don’t let searchers be too lazy!
It’s easy to create miscommunication.

Search suggestions offer strong information scent.
But searcher may not recognize query ambiguity.

Reduce the likelihood of a bait-and-switch: 
only offer high-performance search suggestions.

Should you score search suggestions based 
on total engagement or query performance?

Total engagement is the value delivered by search.
But low query performance means unhappy users.

Can combine as single utility function. Or score using total 
engagement but require minimum query performance.

Finally, let’s remember that search is more than 
a single query and response. It’s a conversation.

Don’t focus so much on single-query performance 
that you forget to optimize for the overall journey.

The best journey is a sequence of small successes. 
Not a sequence of frustrations followed by success.

Summary
• Query understanding means ﬁguring out what the
searcher wants.
• Measure and optimize for query performance.
• Use query rewriting to improve precision and recall, 
in order to improve query performance.
• Query suggestions have two jobs: reduce searcher effort
and improve query performance.
• Search is a conversation.
tl;dr: Query understanding is about focusing 
less on the results and more on the query.

Thank you! Learn more at queryunderstanding.com!

Más contenido relacionado

La actualidad más candente

SEO Restart 2022: Richard Klačko - Investice do SEO? Vypočítejme si potenciál...

Taste

Featured snippets knowledge share

Claire Lagoutte

SEO Restart 2022: Marek Prokop - Analýza klíčových slov v R

Taste

NextJS, A JavaScript Framework for building next generation SPA

Pramendra Gupta

※다운로드하시면 더 선명한 자료를 보실 수 있습니다. 딥러닝을 처음 시작하시는 분, 특히 개발자에게 맞춰 필요한 내용을 준비하였습니다. 딥러닝의 수학적인 이해보다는 딥러닝을 활용하는 방법을 공유하고자 합니다. 목차 1. 딥러닝 권장 개발 환경 2. 코드 작성을 위한 약간의 이론 3. 가장 빠르게 그럴싸한 모델 만들기 대상 딥러닝에 관심이 있거나 딥러닝을 시작하려는 모든 개발자 ※발표 영상은 제공하지 않습니다.

딥러닝, 야 너도 할 수 있어(feat. PyTorch)

NHN FORWARD

【株式会社キャラット様】Social insightご導入後マニュアル210706

YukaKaneshiro

MOBILITY X DATA : 모빌리티 산업의 도전 과제

Seongyun Byeon

Myths, Misconceptions & Mistakes (lessons learned from a decade in digital PR)

Hannah Smith

Tutorial on query auto-completion

Yichen Feng

Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user's need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.

Neural Learning to Rank

Bhaskar Mitra

Web ontology language (owl)

Ameer Sameer

검색엔진이 데이터를 다루는 법 김종민

종민 김

How to Automatically Subcategorise Your Website Automatically With Python

searchsolved

Learned word embeddings such as Word2vec/Glove were initially found to be effective for broad range of tasks in Natural Language Processing (NLP). More recently though, these are being used successfully in areas well beyond text such as graphs and event streams. In this talk Sharath will speak about how we use learned embeddings at Instacart for search ranking, personalization and product recommendations. Presented at: SF Data Mining Meetup https://www.meetup.com/Data-Mining/events/237164197/

Learned Embeddings for Search and Discovery at Instacart

Sharath Rao

Data Studio for SEOs: Reporting Automation Tips - Weekly SEO with Lazarina Stoy

LazarinaStoyanova

A Two Person Panel Discussion/Presentation by Bill Slawski and Barbara Starr On June 23, 2015 The Lotico Semantic Web of San Diego The SEO San Diego Meetup The SEM San Diego Meetup http://www.meetup.com/InternetMarketingSanDiego/events/222788495/ User experience drives search engines, and hence their results. Search Engine Result Presentation/Placements naturally follow that route. This means that search results are no longer exclusively based on just ranking criteria. Amongst other critical factors is understanding the notion of 'ordering vs ranking', the impact of context and many others.

Ranking in Google Since The Advent of The Knowledge Graph

Bill Slawski

RDF validation tutorial

Jose Emilio Labra Gayo

Protege tutorial

Comércio de Portugal

How are you currently doing SERP analysis? Is your approach efficient or scalable? How SEOs can incorporate programmatic approaches and advances in machine learning in order to identify winning strategies? This talk will leave you with a better understanding of what is possible for SERP analysis at scale, what insights you can capture with the help of machine learning quickly, and how to incorporate insights into your strategy and visualize your findings to impress your stakeholders.

How to Incorporate ML in your SERP Analysis, Lazarina Stoy -BrightonSEO Oct, ...

LazarinaStoyanova

서버 성능에 대한 정의와 이해

중선 곽

La actualidad más candente (20)

SEO Restart 2022: Richard Klačko - Investice do SEO? Vypočítejme si potenciál...

Featured snippets knowledge share

SEO Restart 2022: Marek Prokop - Analýza klíčových slov v R

NextJS, A JavaScript Framework for building next generation SPA

딥러닝, 야 너도 할 수 있어(feat. PyTorch)

【株式会社キャラット様】Social insightご導入後マニュアル210706

MOBILITY X DATA : 모빌리티 산업의 도전 과제

Myths, Misconceptions & Mistakes (lessons learned from a decade in digital PR)

Tutorial on query auto-completion

Neural Learning to Rank

Web ontology language (owl)

검색엔진이 데이터를 다루는 법 김종민

How to Automatically Subcategorise Your Website Automatically With Python

Learned Embeddings for Search and Discovery at Instacart

Data Studio for SEOs: Reporting Automation Tips - Weekly SEO with Lazarina Stoy

Ranking in Google Since The Advent of The Knowledge Graph

RDF validation tutorial

Protege tutorial

How to Incorporate ML in your SERP Analysis, Lazarina Stoy -BrightonSEO Oct, ...

서버 성능에 대한 정의와 이해

Destacado

Where should you put your data scientists?

Daniel Tunkelang

Data Science: A Mindset for Productivity Keynote at 2015 Ronin Labs West Coast CTO Summit https://www.eventjoy.com/e/west-coast-cto-summit-2015 Abstract Data science isn't just about using a collection of technologies and algorithms. Data science requires a mindset that solves problems at a higher level of abstraction. How do we model utility when we think about optimization? How do we decide which hypotheses to test? How do we allocate our scarce resources to make progress? There are no silver bullets. But I'll share what I've learned from a variety of contexts over the course of my work at Endeca, Google, and LinkedIn; and I hope you'll leave this talk with some practical wisdom you can apply to your next data science project.

Data Science: A Mindset for Productivity

Daniel Tunkelang

Find and Be Found: Information Retrieval at LinkedIn SIGIR 2013 Industry Track Presentation http://sigir2013.ie/industry_track.html LinkedIn has a unique data collection: the 200M+ members who use LinkedIn are also the most valuable entities in our corpus, which consists of people, companies, jobs, and a rich content ecosystem. Our members use LinkedIn to satisfy a diverse set of navigational and exploratory information needs, which we address by leveraging semi-structured and social content to understanding their query intent and deliver a personalized search experience. In this talk, we will discuss some of the unique challenges we face in building the LinkedIn search platform, the solutions we've developed so far, and the open problems we see ahead of us. Shakti Sinha heads LinkedIn's search relevance team, and has been making key contributions to LinkedIn's search products since 2010. He previously worked at Google as both a research intern and a software engineer. He has an MS in Computer Science from Stanford, as well as a BS degree from College of Engineering, Pune. Daniel Tunkelang leads LinkedIn's efforts around query understanding. Before that, he led LinkedIn's product data science team. He previously led a local search quality team at Google and was a founding employee of Endeca (acquired by Oracle in 2011). He has written a textbook on faceted search, and is a recognized advocate of human-computer interaction and information retrieval (HCIR). He has a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.

Find and be Found: Information Retrieval at LinkedIn

Daniel Tunkelang

Web Science: How is it different? Daniel Tunkelang, LinkedIn Keynote Address at ACM Web Science 2014 Conference The scientific method of observation, measurement, and experiment may be our greatest achievement as a species. The technological innovation we enjoy today is the product of a culture of systematized scientific experimentation. But historically scientific experimentation has been expensive. Experiments consumed natural resources, took a long time to conduct, and required even more time and labor to analyze. In order to be productive, scientists have had to factor these costs into their work and to optimize accordingly. Web science is different. Not, as some have speciously argued, because big data has made the scientific method obsolete. The key difference is that web science has changed the economics of scientific experimentation. Thus, even as web scientists apply the traditional scientific method, they optimize based on very different economics. In this talk, I'll survey how web science has changed our approach to experimentation, for better and for worse. Specifically, I'll talk about differences in hypothesis generation, offline analysis, and online testing. Bio Daniel Tunkelang is Head of Query Understanding at LinkedIn, where he previously formed and led the product data science team. LinkedIn search allows members to find people, companies, jobs, groups and other content. His team aims to provide users with the best possible results that satisfy their information needs and help to get insights from professional data. Tunkelang has BS and MS degrees in computer science and math from MIT, and a PhD in computer science from CMU. He co-founded the annual symposium on human-computer interaction and information retrieval (HCIR) and wrote the first book on Faceted Search (Morgan and Claypool 2009). Prior to joining LinkedIn, Tunkelang was Chief Scientist of Endeca (acquired by Oracle in 2011 for $1.1B) and leader of the local search quality team at Google, mapping local businesses to their home pages. He is the co-inventor of 20 patents.

Web science - How is it different?

Daniel Tunkelang

Enterprise Intelligence: Putting the Pieces Together http://enterpriserelevance.com/kdd2016/keynote.html These slides are for a keynote presentation delivered at the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016). About the author: Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He studied computer science and math at MIT and has a PhD in computer science from CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to "unicorn" technology companies like Etsy and Pinterest. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.

Enterprise Intelligence

Daniel Tunkelang

My Three Ex’s: A Data Science Approach for Applied Machine Learning Daniel Tunkelang (LinkedIn) Presented at QCon San Francisco 2014 in the Applied Machine Learning and Data Science track https://qconsf.com/presentation/my-three-ex%E2%80%99s-data-science-approach-applied-machine-learning Abstract This talk is about applying machine learning to solve problems. It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems. Applied machine learning is more grounded but no less important. We are surrounded by opportunities to apply classifiers, learn rules, compute similarity, and assemble clusters. We don’t need to develop new algorithms for any of these problems — our textbooks and open-source libraries have done that hard work for us. But algorithms are not enough. Applying machine learning to solve problems requires a data science mindset that transcends the algorithmic details. In this talk, I’ll communicate the data science mindset by describing my three ex’s: express, explain, and experiment. These three activities are the pillars of a successful strategy for applying machine learning to solve problems. Whether you’re a machine learning novice or expert, I hope you’ll leave this talk with some practical wisdom you can apply to your next project.

My Three Ex’s: A Data Science Approach for Applied Machine Learning

Daniel Tunkelang

Keynote at CIKM 2013 Workshop on Data-driven User Behavioral Modelling and Mining from Social Media Social Search in a Professional Context Daniel Tunkelang (LinkedIn) Social networks bring a new dimension to search. Instead of looking for web pages or text documents, LinkedIn members search a world of entities connected by a rich graph of relationships. Search is a fundamental part of the LinkedIn ecosystem, as it helps our members find and be found. Unlike most search applications, LinkedIn's search experience is highly personalized: two LinkedIn members performing the same search query are likely to see completely different results. Delivering the right results to the right person depends on our ability to leverage our each member's unique professional identity and network. In this talk, I'll describe the kinds of search behavior we see on LinkedIn, and some of the approaches we've taken to help our members address their information needs.

Social Search in a Professional Context

Daniel Tunkelang

Search as Communication: Lessons from a Personal Journey by Daniel Tunkelang (Head of Query Understanding, LinkedIn) Presented at Etsy's Code as Craft Series on May 21, 2013 When I tell people I spent a decade studying computer science at MIT and CMU, most assume that I focused my studies in information retrieval — after all, I’ve spent most of my professional life working on search. But that’s not how it happened. I learned about information extraction as a summer intern at IBM Research, where I worked on visual query reformulation. I learned how search engines work by building one at Endeca. It was only after I’d hacked my way through the problem for a few years that I started to catch up on the rich scholarly literature of the past few decades. As a result, I developed a point of view about search without the benefit of academic conventional wisdom. Specifically, I came to see search not so much as a ranking problem as a communication problem. In this talk, I’ll explain my communication-centric view of search, offering examples, general techniques, and open problems. -- Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.

Search as Communication: Lessons from a Personal Journey

Daniel Tunkelang

Peters matthew periodictableseo

mattthemathman

Webinar: OpenNLP and Solr for Superior Relevance

Lucidworks

Data analytics applications are often 10x off peak hardware performance since they combine multiple functions from different libraries and frameworks to build increasingly complex workflows. Even if each individual function is optimized in isolation, the cost of data movement across these functions can cause order of magnitude slowdowns. For example, even though the TensorFlow machine-learning library uses highly tuned linear algebra functions for each of its operators, workflows that combine these operators can be 16x slower than hand-tuned code. Similarly, workflows that perform relational processing in Spark SQL or pandas, numerical processing in NumPy, or a combination of these tasks spend most of their time in data movement across processing functions and could run between 2x and 30× faster if optimized end to end. This talk offers an overview of Weld, an optimizing runtime for data-intensive applications that works across disjoint libraries and functions. Weld uses a common representation to capture the structure of diverse data-parallel workloads such as SQL, machine learning, and graph analytics and then optimizes across them using a cost-based optimizer that takes into account hardware characteristics. Weld can be integrated into a variety of widely used analytics frameworks, such as Spark SQL for relational processing, TensorFlow for machine learning, and Pandas and NumPy for general data science workloads. Integrating Weld with these frameworks requires no changes to user application code. Weld speeds up existing workloads in these frameworks by up to 16x and can also enable speed-ups of two orders of magnitude in applications that combine them.

Weld Strata talk

Deepak Narayanan

Deploying deep learning models with Docker and Kubernetes

PetteriTeikariPhD

Destacado (12)

Where should you put your data scientists?

Data Science: A Mindset for Productivity

Find and be Found: Information Retrieval at LinkedIn

Web science - How is it different?

Enterprise Intelligence

My Three Ex’s: A Data Science Approach for Applied Machine Learning

Social Search in a Professional Context

Search as Communication: Lessons from a Personal Journey

Peters matthew periodictableseo

Webinar: OpenNLP and Solr for Superior Relevance

Weld Strata talk

Deploying deep learning models with Docker and Kubernetes

Similar a Query Understanding: A Manifesto

Collaborative Research The Conference by Media Evolution Malmö

Erika Hall

Workshop 1 networking

lorisavage

How to conduct design research interviews

HJ Kwon

The art-and-science-of-interviewing

Corina Neagu, CIPD

Collaborative Research | uxlx 2014

Erika Hall

125 common interview questions and answers

Nicha Tatsaneeyapan

Article marketing secrets

MOMOCACO

Web copywriting workshop

Jennifer Morrow

What do you want to learn? Lean Analytics Cycle AVINASH KAUSHIK HTTP://KISS.LY/LEANAC Lean is all about identifying and quantifying risk. Don’t just ask questions. Know what decisions you can make from these surveys. Lean Analytics Cycle AVINASH KAUSHIK HTTP://KISS.LY/LEANAC This is where we survey 1. Keep surveys short ! Under 5 minutes is ideal Completion Rates Survey Monkey: Does Adding One More Question Impact Survey Completion Rate? Industry Benchmarks 40% is awesome ! 30% is great ! 20% is good ! ~15% is expected ! What We See In Practice Longer Surveys Decrease Completion Rates A 5 Question Screener That I Did Recently 14% CTR from Nudge to Screener ! 202 Responses in 7 days ! 202 Opt-Ins for further studies ! By the way… ! Satisfaction surveys see 10% or lower response rates 2. Start broad then get detailed ! Satisfaction -> specifics Broad questions How satisfied are you? What is your primary motivation? What best describes why you came here today? Specific questions What is your experience with using X? How satisfied are you with X/Y/Z? 3. Ask what you need to know ! Avoid what’s nice to know What do you want to learn? What are you going to DO with this survey data? It’s easy to let additional questions creep up. Resist the urge! 4. Avoid hypothetical questions Would you buy X if we offered it? Instead, ask people about their CURRENT experience, frustrations, or workarounds to their problem. 5. Test survey internally Startups have smaller customer counts. Surveying multiple times gets interpreted as spam. Dry runs with your team are a great way to check if anything is confusing. 3 Ways to Get Surveys In Front of People 2 1. Email ! Apple ’s satisfaction survey uses plain text for simplicity General Assembly’s HTML emails look too automated Plain text works best. ! Looks like a real person wrote it. 2. In-App Opt-In ! Google Has Dedicated Usability Opt Ins Twitter Integrates In-App Opt-In Into Their Apps Target Opt-Ins Based On People’s Behavior To Increase Chances of Response 2. In-App Pop-up ! GQ Asks about Subscription Friction Logitech Asks for Use Cases 3 Strategies for Segmenting Survey Results 3 Demographic segmentation is the norm, but only if it actually helps. 1. Platform ! Segmenting by Device Type Another Example of Segmenting by Device Type 2. Time of Survey Response ! Segmenting by Time Survey Was Completed Zendesk Uses Time of Survey To Send Surveys At Times That Have Higher Response Rates Zendesk Figured Out “Not Work Hours” Worked Best 3. Cross-Reference with Analytics ! Cross Reference Analytics Data with Survey Data to Get the Whole Picture Features used / products bought ! Frequency of visits ! Our Favorite Tools and Workflows 4 Research Tools of the Trade Our toolkit for getting survey’s done. Book Resource: Improving Survey Questions: Design and Evaluation

How to Get Survey Answers That Actually Help Your Business

Kissmetrics on SlideShare

Những câu hỏi thường gặp khi đi phỏng vấn

Đinh Công Thiện Taydo University

Tips - How to interview a candidate

K K Consulting Services

Career path non technical

Balakumaran Arunachalam

How to Get an Entry-Level Job with No Experience - ej4

ej4video

With so many elements going into constructing a successful website, it’s crucial to know what to look for when evaluating what users want. So what’s the best way to test your website’s effectiveness? We were wondering something similar, so we conducted a little experiment of our own. We’ll show you the step-by-step process for conducting experiments on your website, so you can get the important data you need to keep your website strong. You’ll learn: - The best time to run an experiment to get the most informative data. - What to do when you don't see the results you expect, and how to avoid other factors influencing your results. - How we run our experiments, and what we plan to do with the results. Watch our very own Angie Nikoleychuk, Content Marketing Manager, and learn the key factors to focus on when running website experiments, so your users stay happy and engaged. You’ll also learn the results of our own case study, where we examined ad performance and user happiness on our website.

How to Perform Website Experiments [+ SEJ Experiment Walk-Through & Results]

Search Engine Journal

The best answers to tough interview questions

EDIN BROW, DCE, AMET

P.6. the professional interview

Paul J. Walsh

Collaborative Research

Erika Hall

Everyone hopes to land an interview or get an opportunity to show themselves to their future employers. What if you swapped roles and YOU conducted the interview? Don't wait for an invitation to interview, make your own! Learn the tactics involved in information Interviewing. Source Employers, ask intentional questions to make your opportunity count and follow up like a professional to advance the relationship. If you follow these simple steps, you can information Interview your way to a job!

Information Interview your way to a Job Handout

Jeff Watson

KWFinder Review

Noel Peter

Hrm presentation topic

Mahnoor Hashmi

Similar a Query Understanding: A Manifesto (20)

Collaborative Research The Conference by Media Evolution Malmö

Workshop 1 networking

How to conduct design research interviews

The art-and-science-of-interviewing

Collaborative Research | uxlx 2014

125 common interview questions and answers

Article marketing secrets

Web copywriting workshop

How to Get Survey Answers That Actually Help Your Business

Những câu hỏi thường gặp khi đi phỏng vấn

Tips - How to interview a candidate

Career path non technical

How to Get an Entry-Level Job with No Experience - ej4

How to Perform Website Experiments [+ SEJ Experiment Walk-Through & Results]

The best answers to tough interview questions

P.6. the professional interview

Collaborative Research

Information Interview your way to a Job Handout

KWFinder Review

Hrm presentation topic

Más de Daniel Tunkelang

Query Understanding and Ecommerce

Daniel Tunkelang

Semantic Equivalence of e-Commerce Queries

Daniel Tunkelang

Behavioral economics transformed how we think about human decision making, rejecting expected utility maximization for the real world of heuristics, biases, and satisficing. In this talk, I'll argue that our thinking about search engines needs a similar transformation. I will compare the Probability Ranking Principle to expected utility maximization and offer ways that AI can help searchers satisfice through query understanding. This was an invited talk given at the 2023 Walmart AI Summit. Speaker Bio Daniel Tunkelang is an independent consultant specializing in search, machine learning / AI, and data science. He completed undergraduate and master's degrees in Computer Science and Math at MIT and a PhD in computer science at CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired in 2011. He then led engineering and data science teams at Google and LinkedIn. He has written a book on Faceted Search, and he blogs on Medium about search-related topics — particularly query understanding. He has worked with numerous tech companies, retailers, and others, including Algolia, Apple, Canva, Coupang, eBay, Etsy, Flipkart, Home Depot, Oracle, Pinterest, Salesforce, Target, Yelp, and Zoom.

Helping Searchers Satisfice through Query Understanding

Daniel Tunkelang

MMM, Search! An opinionated discussion of search metrics, models, and methods. Presented to the Wikimedia Foundation on April 27, 2020. About the Speaker Daniel Tunkelang is an independent consultant specializing in search, discovery, machine learning / AI, and data science. He was a founding employee of Endeca, a search pioneer that Oracle acquired. After 10 years at Endeca, he moved to Google, where he led a local search team. He then served as a director of data science and search at LinkedIn. After leaving LinkedIn in 2015, he became an independent consultant. His clients have included Apple, eBay, Coupang, Etsy, Flipkart, Gartner, Pinterest, Salesforce, and Yelp; as well as some of the largest traditional retailers. Daniel completed undergraduate and master's degrees in Computer Science and Math at MIT and a Ph.D. in computer science at CMU. He wrote a book on Faceted Search, published by Morgan & Claypool, and he blogs on Medium about search-related topics -- particularly about query understanding. He is also active on Twitter, LinkedIn, and Quora.

MMM, Search!

Daniel Tunkelang

Enterprise Search: How Do We Get There From Here? by Daniel Tunkelang (Head of Query Understanding, LinkedIn) Keynote at 2013 Enterprise Search Summit We've been tackling the challenges of enterprise and site search for at least 3 decades. We've succeeded to the point that search is the gateway to many of our information repositories. Nonetheless, users of enterprise search systems are frustrated with these systems' shortcomings. We see this frustration in surveys, but, more importantly, most of us experience it personally in our daily work life. We all dream of a world where searching any information repository is as effective as searching the web—perhaps even more so. A world where we find what we're looking for, or quickly determine that it doesn't exist. Is this Utopia possible? If so, how do we get there from here? Or at least somewhere close? In this talk, Tunkelang reviews the track record of enterprise search. He talks about what's worked and what hasn't, especially as compared to web search. Finally, he proposes some paths to bring us closer to our dream. -- Daniel Tunkelang is Head of Query Understanding at LinkedIn. Educated at MIT and CMU, he has his career working on big data, addressing key challenges in search, data mining, user interfaces, and network analysis. He co-founded enterprise search and business intelligence pioneer Endeca, where he spent a decade as its Chief Scientist. In 2011, Endeca was acquired by Oracle for over $1B. Previous to LinkedIn, he led a team at Google working on local search quality. Daniel has authored fifteen patents, written a textbook on faceted search, and created the annual symposium on human-computer interaction and information retrieval.

Enterprise Search: How do we get there from here?

Daniel Tunkelang

Big Data, We Have a Communication Problem by Daniel Tunkelang Presented on April 30, 2013 at the TTI/Vanguard Conference on Ginormous Systems http://www.ttivanguard.com/conference/2013/ginormous.html It's a cliché that we live in a world of Big Data. But the bottleneck in understanding data is not computational. Rather, the biggest challenge is designing technical solutions that effectively leverage human cognitive ability. Data analysis systems should augment people's capabilities rather than replace them. This argument is as old as computer science itself: in 1962, Doug Engelbart said that the goal of technology is “the enhancement of human intellect by increasing the capability of a human to approach a complex problem situation.” Algorithms extract signal from raw data, but people fill in the gaps, creating models and evaluating analyses. Empowering people to understand data is not just a surface problem of building better interfaces and visualizations. We need to interact with data not only after performing computational analysis, but throughout the analysis process in order to improve our models and algorithms. In order to do so, we need tools and processes specifically designed to offer people transparency, guidance, and control. Human-computer information retrieval has been revolutionizing our approach to information seeking -- no modern search engine limits users to black-box relevance ranking and ten blue links. We need to take similar steps in our analysis of big data, making people the center of the analysis process and developing the technical innovations that enable people to fulfill this role.

Big Data, We Have a Communication Problem

Daniel Tunkelang

How To Interview a Data Scientist Daniel Tunkelang Presented at the O'Reilly Strata 2013 Conference Video: https://www.youtube.com/watch?v=gUTuESHKbXI Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy. At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work. In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.

How to Interview a Data Scientist

Daniel Tunkelang

Information, Attention, and Trust: A Hierarchy of Needs

Daniel Tunkelang

Data By The People, For The People Daniel Tunkelang Director, Data Science at LinkedIn Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012) LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful. Bio: Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.

Data By The People, For The People

Daniel Tunkelang

Content, Connections, and Context Daniel Tunkelang, LinkedIn Keynote at Workshop on Recommender Systems and the Social Web At 6th ACM International Conference on Recommender Systems (RecSys 2012) Recommender systems for the social web combine three kinds of signals to relate the subject and object of recommendations: content, connections, and context. Content comes first - we need to understand what we are recommending and to whom we are recommending it in order to decide whether the recommendation is relevant. Connections supply a social dimension, both as inputs to improve relevance and as social proof to explain the recommendations. Finally, context determines where and when a recommendation is appropriate. I'll talk about how we use these three kinds of signals in LinkedIn's recommender systems, as well as the challenges we see in delivering social recommendations and measuring their relevance.

Content, Connections, and Context

Daniel Tunkelang

Keynote at 2012 Semantic Technology and Business Conference Scale, Structure, and Semantics Daniel Tunkelang, LinkedIn Science fiction has a mixed track record when it comes to anticipating technological innovations. While Jules Verne fared well with with his predictions of submarine and space technology, artificial intelligence hasn't produced anything like Arthur C. Clarke's HAL 9000. Instead, we've managed to elicit intelligence from machines through unexpected means. Search engines have achieved remarkable success in organizing the world's information by crawling the web, indexing documents, and exploiting link structure to establish authoritativeness. At LinkedIn, we apply large-scale analytics to terabytes of semistructured data to deliver products and insights that serve our 150M+ members. Semantics emerge when we apply the right analytical techniques to a sufficient quality and quantity of data. In this talk, I will describe how LinkedIn's huge and rich graph of relationship data that powers the products our users love. I believe that the lessons we have learned apply broadly to other semantic applications. While quantity and quality of data are the key challenges to delivering a semantically rich experience, the key is to create the right ecosystem that incents people to give you good data, which then forms the basis for great data products.

Scale, Structure, and Semantics

Daniel Tunkelang

Presentation from O'Reilly Strata 2012 on Big Data Humans, Machines, and the Dimensions of Microwork Daniel Tunkelang (LinkedIn) Claire Hunsaker (Samasource) The advent of crowdsourcing has wildly expanded the ways we think of incorporating human judgments into computational workflows. Computer scientists, economists, and sociologists have explored how to effectively and efficiently distribute microwork tasks to crowds and use their work as inputs to create or improve data products. Simultaneously, crowdsourcing providers are exploring the bounds of mechanical QA flows, worker interfaces, and workforce management systems. But what tasks should be performed by humans rather than algorithms? And what makes a set of human judgments robust? Quantity? Consensus? Quality or trustworthiness of the workers? Moreover, the robustness of judgments depends not only on the workers, but on the task design. Effective crowdsourcing is a cooperative endeavor. In this talk, we will analyze various dimensions of microwork that characterize applications, tasks, and crowds. Drawing on our experience at companies that have pioneered the use of microwork (Samasource) and data science (LinkedIn), we will offer practical advice to help you design crowdsourcing workflows to meet your data product needs.

Strata 2012: Humans, Machines, and the Dimensions of Microwork

Daniel Tunkelang

These slides are from a tutorial at the 5th ACM International Conference on Recommender Systems (RecSys 2011). Recommender systems aim to provide users with products or content that satisfy the users' stated or inferred needs. The primary evaluation measures for recommender systems emphasize either the perceived relevance of the recommendations or the actions associated with those recommendations (e.g., purchases or clicks). Unfortunately, this transactional emphasis neglects how users interact with recommendations in the context of information seeking tasks. The effectiveness of this interaction determines the user's experience beyond a single transaction. This tutorial explores the role of recommendations as part of a conversation between the user and an information seeking system. The tutorial does not require any special background in interfaces or usability, and will focus on practical techniques to make recommender systems most effective for users.

Recommendations as a Conversation with the User

Daniel Tunkelang

Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn Daniel Tunkelang (LinkedIn) LinkedIn operates the world's largest professional network on the Internet with more than 100 million members in over 200 countries. In order to connect its users to the people, opportunities, and content that best advance their careers, LinkedIn has developed a variety of algorithms that surface relevant content, offer personalized recommendations, and establish topic-sensitive reputation -- all at a massive scale. In this talk, I will discuss some of the most challenging technical problems we face at LinkedIn, and the approaches we are taking to address them. Note: This talk was presented at the Carnegie Mellon University School of Computer Science Intelligence Seminar on September 20, 2011. As of May 2013, LinkedIn has over 225 million members.

Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn

Daniel Tunkelang

The War on Attention Poverty: Measuring Twitter Authority As social networks like Facebook and Twitter have grown in popularity, we've had ample opportunity to appreciate Herb Simon's admonition that "a wealth of information creates a poverty of attention". Since there is no way we can hope to follow all of the information being shared by our social networks, we need some filtering or ranking mechanism. A broad class of approaches involves determining which authors are the most authoritative or influential. There are already a variety of proposed authority measures, as well as research on their effectiveness. In this talk, I will review the various attempts that have been made to measure Twitter authority. In particular, I will discuss the work on TunkRank, a measure inspired by PageRank that explicitly models attention scarcity.

The War on Attention Poverty: Measuring Twitter Authority

Daniel Tunkelang

Design for Interaction by Daniel Tunkelang, Chief Scientist of Endeca An invited presentation at SIGMOD '09 (http://sigmod09.org/) Research in information retrieval has focused on presenting the most relevant results to a user in response to a free-text search query. Research in database systems assumes a model where the user enters a formal query, and the results are exactly those the user requested. Neither community has emphasized user interaction—a critical concern for practical information access. As William Goffman noted in the 1960s and Nick Belkin continually reminds us today, the relationship between a document and query, though necessary, is not sufficient to determine relevance—yet ranked retrieval approaches rely heavily or exclusively on this relationship. Meanwhile, recent work on database usability by Jeff Naughton and H.V. Jagadish surfaces the rigidity of database systems that return nothing unless users know how to formulate precise queries. This talk presents human-computer information retrieval (HCIR) as a general approach that addresses some of the key challenges facing both research communities. A vision first put forward by Gary Marchionini, HCIR expects people and systems to work together to implement information access. Such an approach requires rethinking information access not as a matching or ranking problem, but rather as a communication problem. Specifically, we need interfaces that optimize the bidirectional communication between the user and the system, thus optimizing the symbiotic division of labor between the two. This talk reviews the history of HCIR efforts and presents ongoing work to implement the HCIR vision. In particular, it presents an interactive set retrieval approach that responds to queries with an overview of the user's current context and an organized set of options for incremental exploration.

Design for Interaction

Daniel Tunkelang

Enterprises are awash in textual documents that represent valuable information assets. The limited access of conventional search interfaces, however, prevents enterprises from unlocking this value; * An expert guide to how richer interfaces enable exploration and discovery and how these typically rely on content enrichment techniques that can be unreliable, labor-intensive, or both. It is essential to maximize the effectiveness of content enrichment, not only to achieve the desired value, but also to incent organizations to make the necessary investment. * Useful insight about content enrichment approaches that have demonstrated success in supporting exploration and discovery. * Gain insight into both the enrichment techniques and the ways they are used to enable exploratory search. Daniel Tunkelang, Chief Scientist, Endeca

Enabling Exploration Through Text Analytics

Daniel Tunkelang

exploring semantic means Daniel Tunkelang, Chief Scientist of Endeca Endeca is a leading provider of enterprise information access. While Endeca is not a “semantic web” company (we’re more of an XML / XQuery shop), we share Tim Berners-Lee’s dream of exposing the semantic content of data to reduce the tedious and brittles processes that people use today in order to meet their information needs, whether on the web or in the enterprise. Our emphasis is on exploratory search, as contrasted with the “10 blue links” approach that characterizes conventional search engines. Come join us for a tour of how we are enabling a conversation between humans and data through content enrichment and set-oriented retrieval and analysis.

exploring semantic means

Daniel Tunkelang

This presentation outlines the principles of information seeking as a dialogue and walk though concrete examples that illustrate the principles of human-computer information retrieval (HCIR). The foundation is an interactive set retrieval approach that responds to queries with an overview of the user\'s current context and an organized set of options for incremental exploration. Contextual summaries of document sets optimize the system\'s communication with the user, while query refinement options optimize user\'s communication with the system. By enabling bidirectional communication between the user and the system, we can address the inherent limitations of best-match approaches.

Set Retrieval 2.0

Daniel Tunkelang

Guided Summarization

Daniel Tunkelang

Más de Daniel Tunkelang (20)

Query Understanding and Ecommerce

Semantic Equivalence of e-Commerce Queries

Helping Searchers Satisfice through Query Understanding

MMM, Search!

Enterprise Search: How do we get there from here?

Big Data, We Have a Communication Problem

How to Interview a Data Scientist

Information, Attention, and Trust: A Hierarchy of Needs

Data By The People, For The People

Content, Connections, and Context

Scale, Structure, and Semantics

Strata 2012: Humans, Machines, and the Dimensions of Microwork

Recommendations as a Conversation with the User

Keeping It Professional: Relevance, Recommendations, and Reputation at LinkedIn

The War on Attention Poverty: Measuring Twitter Authority

Design for Interaction

Enabling Exploration Through Text Analytics

exploring semantic means

Set Retrieval 2.0

Guided Summarization

Último

We specialize in Psychic Readings, Psychic Love Spells, Binding Love Spells, Obsession Spells, Voodoo Spells, Lottery Spells, Marriage Spells, Black Magic Spells, Palm Readings & much more. Are you depressed? We perform this come-to-me love spell that works instantly with the aim of bringing back the victim to the person performing the magic. Have you lost your lover? We perform this come-to-me love spell that works instantly with the aim of bringing back the victim to the person performing the magic. Have you lost your lover? Do u need to solve any relationship problem? Contact the powerful spells caster chief kule with love spells that work overnight and love spells that really work. Have you found yourself infatuated with a special someone you think could be the one? Are you looking for a spell to provide them with a nudge in the right direction? Or maybe the spell you cast didn’t achieve the results you were hoping for? Whether you’re new or versed in the ways of spell casting, we’re here to help. Today we’re going to provide you with a detailed guide on the types of love spells to cast. Not only that but there’s something for those who wish to find outside advice from more advanced spell casters. We’re also going to provide you with the top sites available to help you with your dilemma. Let’s begin our journey by educating ourselves on love magic and what a real love caster looks like. Love Magic and Love Casters Love magic made its first appearance back in Ancient Egypt and has been an active practice since. This type of magic is a branch of traditional magic and can be practiced in various ways. Typically the more common use of love magic is through the work of spells, but other methods look like Charms Rituals-LOVE Potions-Dolls and even Amulets If you are interested in becoming a love caster, be prepared for what’s to come. A genuine love caster knows that the art of love casting is no easy feat and shouldn’t be done casually. You should know that not only does it require you to be gifted spiritually, but you must be ready to serve others. Someone who is considered a real love caster has experience in all manner of spells, no matter the difficulty. Training yourself in attraction, commitment, and marriage spells is an excellent place to start. But this by no means will make you a professional. Practice your craft and expand your knowledge; understand that you will possess the ability to help others in time truly. Types of Love Spells What better way to start broadening your experiences with love spells than by learning more about them? These spells work like just about any other spell. Simply apply your intention, use a medium (sigils, mantras, candles, or charm bags), and top it off with establishing the belief that you will receive what you want. So what kind of spells are available and which ones suit your needs the best? Let’s take a look at the many options you have at your disposal.

%+27788225528 love spells in Atlanta Psychic Readings, Attraction spells,Brin...

masabamasaba

WSO2Con204 - Hard Rock Presentation - Keynote

WSO2

Craft an AI & Machine Learning Pitch with our Editable Professional PowerPoint Template. Ignite your AI & Machine Learning pitch with our cutting-edge PowerPoint template tailored for the industry. Perfect for AI conferences, investor presentations, sales pitches to tech-focused companies, training sessions, and educational programs. - 20+ editable slides: Get a variety of options to choose from for your presentation. - Time-saving solution: Download, replace text/images with a few clicks. - User-friendly customization: Easy to use and personalize. - Modern and attractive design: Captivating visuals, sleek layout. - Tailored to your requirements: Fully alterable for customization. - Well-organized slides: Complete control over content. - Thematic specificity: Reflects healthcare industry with relevant graphics. - Showcase your business idea: Communicate value proposition effectively.

AI & Machine Learning Presentation Template

Presentation.STUDIO

Crypto Cloud Review - How To Earn Up To $500 Per DAY Of Bitcoin 100% On AutoP...

SelfMade bd

%in Stilfontein+277-882-255-28 abortion pills for sale in Stilfontein

masabamasaba

We specialize in Psychic Readings, Psychic Love Spells, Binding Love Spells, Obsession Spells, Voodoo Spells, Lottery Spells, Marriage Spells, Black Magic Spells, Palm Readings & much more. Are you depressed? We perform this come-to-me love spell that works instantly with the aim of Winnipeg back the victim to the person performing the magic. Have you lost your lover? We perform this come-to-me love spell that works instantly with the aim of bringing back the victim to the person performing the magic. Have you lost your lover? Do u need to solve any relationship problem? Contact the powerful spells caster chief kule with love spells that work overnight and love spells that really work. Have you found yourself infatuated with a special someone you think could be the one? Are you looking for a spell to provide them with a nudge in the right direction? Or maybe the spell you cast didn’t achieve the results you were hoping for? Whether you’re new or versed in the ways of spell casting, we’re here to help. Today we’re going to provide you with a detailed guide on the types of love spells to cast. Not only that but there’s something for those who wish to find outside advice from more advanced spell casters. We’re also going to provide you with the top sites available to help you with your dilemma. Let’s begin our journey by educating ourselves on love magic and what a real love caster looks like. Love Magic and Love Casters Love magic made its first appearance back in Ancient Egypt and has been an active practice since. This type of magic is a branch of traditional magic and can be practiced in various ways. Typically the more common use of love magic is through the work of spells, but other methods look like Charms Rituals-LOVE Potions-Dolls and even Amulets If you are interested in becoming a love caster, be prepared for what’s to come. A genuine love caster knows that the art of love casting is no easy feat and shouldn’t be done casually. You should know that not only does it require you to be gifted spiritually, but you must be ready to serve others. Someone who is considered a real love caster has experience in all manner of spells, no matter the difficulty. Training yourself in attraction, commitment, and marriage spells is an excellent place to start. But this by no means will make you a professional. Practice your craft and expand your knowledge; understand that you will possess the ability to help others in time truly. Types of Love Spells What better way to start broadening your experiences with love spells than by learning more about them? These spells work like just about any other spell. Simply apply your intention, use a medium (sigils, mantras, candles, or charm bags), and top it off with establishing the belief that you will receive what you want. So what kind of spells are available and which ones suit your needs the best? Let’s take a look at the many options you have at your disposal. Attraction Spells

%+27788225528 love spells in Huntington Beach Psychic Readings, Attraction sp...

masabamasaba

Artyushina_Guest lecture_YorkU CS May 2024.pptx

AnnaArtyushina1

Abortion Pill Prices Tembisa [(+27832195400*)] 🏥 Women's Abortion Clinic in Tembisa ● Abortion Pills For Sale in Tembisa ● Tembisa 🏥🚑!! Abortion Clinic Near Me Cost, Price, Women's Clinic Near Me, Abortion Clinic Near, Abortion Doctors Near me, Abortion Services Near Me, Abortion Pills Over The Counter, Abortion Pill Doctors' Offices, Abortion Clinics, Abortion Places Near Me, Cheap Abortion Places Near Me, Medical Abortion & Surgical Abortion, approved cyctotec pills and womb cleaning pills too plus all the instructions needed This Discrete women’s Termination Clinic offers same day services that are safe and pain free, we use approved pills and we clean the womb so that no side effects are present. Our main goal is that of preventing unintended pregnancies and unwanted births every day to enable more women to have children by choice, not chance. We offer Terminations by Pill and The Morning After Pill.” Our Private VIP Abortion Service offers the ultimate in privacy, efficiency and discretion. we do safe and same day termination and we do also womb cleaning as well its done from 1 week up to 28 weeks. We do delivery of our services world wide SAFE ABORTION CLINICS/PILLS ON SALE WE DO DELIVERY OF PILLS ALSO Abortion clinic at very low costs, 100% Guaranteed and it’s safe, pain free and a same day service. It Is A 45 Minutes Procedure, we use tested abortion pills and we do womb cleaning as well. Alternatively the medical abortion pill and womb cleansing !!!

Abortion Pill Prices Tembisa [(+27832195400*)] 🏥 Women's Abortion Clinic in T...

Medical / Health Care (+971588192166) Mifepristone and Misoprostol tablets 200mg

WSO2CON 2024 Slides - Open Source to SaaS

WSO2

%in kaalfontein+277-882-255-28 abortion pills for sale in kaalfontein

masabamasaba

+971565801893 Mtp-Kit (500MG) Prices » Dubai [(+971565801893**)] Abortion Pills For Sale In Dubai, UAE, Mifepristone and Misoprostol Tablets Available In Dubai, UAE CONTACT DR.Leen Whatsapp +971565801893 We Have Abortion Pills / Cytotec Tablets /Mifegest Kit Available in Dubai, Sharjah, Abudhabi, Ajman, Alain, Fujairah, Ras Al Khaimah, Umm Al Quwain, UAE, Buy cytotec in Dubai +971565801893''''Abortion Pills near me DUBAI | ABU DHABI|UAE. Price of Misoprostol, Cytotec” +971565801893' Dr.DEEM ''BUY ABORTION PILLS MIFEGEST KIT, MISOPROTONE, CYTOTEC PILLS IN DUBAI, ABU DHABI,UAE'' Contact me now via What's App…… abortion Pills Cytotec also available Oman Qatar Doha Saudi Arabia Bahrain Above all, Cytotec Abortion Pills are Available In Dubai / UAE, you will be very happy to do abortion in Dubai we are providing cytotec 200mg abortion pill in Dubai, UAE. Medication abortion offers an alternative to Surgical Abortion for women in the early weeks of pregnancy. We only offer abortion pills from 1 week-6 Months. We then advise you to use surgery if its beyond 6 months. Our Abu Dhabi, Ajman, Al Ain, Dubai, Fujairah, Ras Al Khaimah (RAK), Sharjah, Umm Al Quwain (UAQ) United Arab Emirates Abortion Clinic provides the safest and most advanced techniques for providing non-surgical, medical and surgical abortion methods for early through late second trimester, including the Abortion By Pill Procedure (RU 486, Mifeprex, Mifepristone, early options French Abortion Pill), Tamoxifen, Methotrexate and Cytotec (Misoprostol). The Abu Dhabi, United Arab Emirates Abortion Clinic performs Same Day Abortion Procedure using medications that are taken on the first day of the office visit and will cause the abortion to occur generally within 4 to 6 hours (as early as 30 minutes) for patients who are 3 to 12 weeks pregnant. When Mifepristone and Misoprostol are used, 50% of patients complete in 4 to 6 hours; 75% to 80% in 12 hours; and 90% in 24 hours. We use a regimen that allows for completion without the need for surgery 99% of the time. All advanced second trimester and late term pregnancies at our Tampa clinic (17 to 24 weeks or greater) can be completed within 24 hours or less 99% of the time without the need surgery. The procedure is completed with minimal to no complications. Our Women's Health Center located in Abu Dhabi, United Arab Emirates, uses the latest medications for medical abortions (RU-486, Mifeprex, Mifegyne, Mifepristone, early options French abortion pill), Methotrexate and Cytotec (Misoprostol). The safety standards of our Abu Dhabi, United Arab Emirates Abortion Doctors remain unparalleled. They consistently maintain the lowest complication rates throughout the nation. Our Physicians and staff are always available to answer questions and care for women in one of the most difficult times in their lives. The decision to have an abortion at the Abortion Clinic in Abu Dhabi, United Arab Emirates.+971565801893

+971565801893>>SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHAB...

Health

Devoxx UK 2024 - Going serverless with Quarkus, GraalVM native images and AWS...

Bert Jan Schrijver

WSO2CON 2024 - API Management Usage at La Poste and Its Impact on Business an...

WSO2

Abortion Pills In Pretoria ](+27832195400*)[ 🏥 Women's Abortion Clinic In Pretoria ● Abortion Pills For Sale In Pretoria ● Pretoria 🏥🚑!! Abortion Clinic Near Me Cost, Price, Women's Clinic Near Me, Abortion Clinic Near, Abortion Doctors Near me, Abortion Services Near Me, Abortion Pills Over The Counter, Abortion Pill Doctors' Offices, Abortion Clinics, Abortion Places Near Me, Cheap Abortion Places Near Me, Medical Abortion & Surgical Abortion, approved cyctotec pills and womb cleaning pills too plus all the instructions needed This Discrete women’s Termination Clinic offers same day services that are safe and pain free, we use approved pills and we clean the womb so that no side effects are present. Our main goal is that of preventing unintended pregnancies and unwanted births every day to enable more women to have children by choice, not chance. We offer Terminations by Pill and The Morning After Pill.” Our Private VIP Abortion Service offers the ultimate in privacy, efficiency and discretion. we do safe and same day termination and we do also womb cleaning as well its done from 1 week up to 28 weeks. We do delivery of our services world wide SAFE ABORTION CLINICS/PILLS ON SALE WE DO DELIVERY OF PILLS ALSO Abortion clinic at very low costs, 100% Guaranteed and it’s safe, pain free and a same day service. It Is A 45 Minutes Procedure, we use tested abortion pills and we do womb cleaning as well. Alternatively the medical abortion pill and womb cleansing !!!

Abortion Pills In Pretoria ](+27832195400*)[ 🏥 Women's Abortion Clinic In Pre...

Medical / Health Care (+971588192166) Mifepristone and Misoprostol tablets 200mg

ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

Architecture decision records - How not to get lost in the past

Papp Krisztián

MarTech Trend 2024 Book : Marketing Technology Trends (2024 Edition) How Data...

Jittipong Loespradit

OpenChain - The Ramifications of ISO/IEC 5230 and ISO/IEC 18974 for Legal Pro...

Shane Coughlan

Love witchcraft +27768521739 Binding love spell in Sandy Springs, GA |psychic near me by a powerful astrologer and spell caster in Atlanta. Bring back your lover with Asaf's voodoo-love witchcraft. Psychic Reading | Astrologer | Spell Caster | Obsession Spells | Black Magic | Witchcraft | Protection spell | wiccan spells. Black magic expert and voodoo love spells that work overnight to retrieve that love | lost love spell caster with powerful love psychic reading. Best astrologer & psychic in Sandy Springs, GA to renew your relationship & make your relationship stronger. love spells to bring back the feelings of love for ex-lovers. Increase the intimacy, affection & love between you and your lover using voodoo relationship obsession spells in USA. Money spells, easy love spells with just words, think of me spell, powerful love spell, spells of love, spells that work, love potion to attract a man, easy love spells with just words, pink candle prayer, white magic spells, call me spell, manifestation spell, gay love spells, Commitment spells, business spells and, how to bring back lost love in a relationship, Witchcraft love spells that work immediately to increase love & intimacy in your relationship. Attraction love spells to attract someone, stop a divorce, prevent a breakup & get your ex back. When using love binding spells, there are several things you should know, particularly how to protect yourself from negative energies and how to use the powers of incantations for the good of all people involved. When the focus is love, the results are truly magical. It’s important to remember love is not manipulative, it is not forceful, and it does not bend another to its will. Love is free-flowing, accepting, kind, and generous. For your love spell to work as intended, you must have good intentions in your heart. Below, we share the top five love spells you can use today to shift the energies in your life and create a future filled with love and fulfilment. Obsession spells to Get Your Ex-Lover Back. All women want one thing the most in life to be love and love in return – not much to ask. A lady wants a good man who loves you unconditionally and loyally. You want a man who is honest, hardworking, and loyal – not a complainer or a weakling or a self-centred man. You are a strong, independent, sensual, caring, loving woman. And you don’t think it’s asking too much to want to be with a loving, intelligent man with a good sense of humour and daring, an upright and confident man – who knows who he is. No one wants a chauvinistic and macho man, but you do want someone who will be willing to protect and care for you. Who loves you for you and not some kind of imaginary. You have no doubt that when the right guy appears on your doorstep, you’ll never let him go. But sometimes a man doesn’t realize he has that good woman.

Love witchcraft +27768521739 Binding love spell in Sandy Springs, GA |psychic...

chiefasafspells

%+27788225528 love spells in Toronto Psychic Readings, Attraction spells,Brin...

masabamasaba

Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid

Philip Schwarz

Query Understanding: A Manifesto

1. Query Understanding: A Manifesto Daniel Tunkelang queryunderstanding.com

2. Overview • What is query understanding? • Query performance prediction. • Query rewriting. • Query suggestions. • Search is a conversation. tl;dr: Query understanding is about focusing  less on the results and more on the query.

3. Query understanding means ﬁguring out what the searcher wants.

4. What query understanding is not: scoring and ranking of results.

5. Query understanding means not relying  on ranking to ﬁlter out irrelevant results.

6. Challenge: accurately identify searcher's intent  while minimizing searcher’s effort.

7. Old-school search:  searcher enters query, search engine interprets query.

8. Modern search:  searcher starts to type, search engine suggests queries.

9. And in 2016, modern search means mobile search.

10. March Hare: “You should say what you mean.”  Alice: “I do. At least I mean what I say.”

11. But what if the search engine doesn’t  know what the searcher means?

12. And how does a search engine achieve the  self-awareness to know what it doesn’t know?

13. Query understanding is about measuring  and optimizing for query performance.

14. History is the best teacher. For common queries, learn from historical performance.

15. How you deﬁne performance depends on why searchers use your site. But deﬁne it and measure it!

16. Can also learn from similar queries: re-ordered terms, subqueries, similar results, etc.

17. Stuck in the long tail?  Predict performance from query and result set features.

18. Lots of academic work on query performance prediction.

19. So you have a poorly performing query. Now what? Rewrite it!

20. Rewriting to improve precision: query segmentation, ﬁeld restriction, query classiﬁcation. tag: skill OR title related skills: search, ranking, … tag: company id: 1337 industry: internet verticals: people, jobs intent: exploratory

21. Rewriting to improve recall: query expansion, query relaxation.

22. When query expansion is done right, users barely notice. Query relaxation is risker and should be messaged.

23. Query rewriting should improve query performance. If it doesn’t, you’re doing wrong. Measure!

24. Rewriting is great when searchers type whole queries. But what about autocomplete and search suggestions?

25. Query suggestions have two jobs: reduce searcher effort and improve query performance.

26. Searchers are people. People are lazy. All else equal, help them type as little as possible.

27. But don’t let searchers be too lazy! It’s easy to create miscommunication.

28. Search suggestions offer strong information scent. But searcher may not recognize query ambiguity.

29. Reduce the likelihood of a bait-and-switch:  only offer high-performance search suggestions.

30. Should you score search suggestions based  on total engagement or query performance?

31. Total engagement is the value delivered by search. But low query performance means unhappy users.

32. Can combine as single utility function. Or score using total  engagement but require minimum query performance.

33. Finally, let’s remember that search is more than  a single query and response. It’s a conversation.

34. Don’t focus so much on single-query performance  that you forget to optimize for the overall journey.

35. The best journey is a sequence of small successes.  Not a sequence of frustrations followed by success.

36. Summary • Query understanding means ﬁguring out what the searcher wants. • Measure and optimize for query performance. • Use query rewriting to improve precision and recall,  in order to improve query performance. • Query suggestions have two jobs: reduce searcher effort and improve query performance. • Search is a conversation. tl;dr: Query understanding is about focusing  less on the results and more on the query.

37. Thank you! Learn more at queryunderstanding.com!

Query Understanding: A Manifesto

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (12)

Similar a Query Understanding: A Manifesto

Similar a Query Understanding: A Manifesto (20)

Más de Daniel Tunkelang

Más de Daniel Tunkelang (20)

Último

Último (20)

Query Understanding: A Manifesto