Query Understanding: A Manifesto

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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!

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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

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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

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