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Daniel Tunkelang
Head, Query Understanding
better search thro...
overview
 query understanding: what is it?
 how we do query understanding at LinkedIn
 some other thoughts from search ...
Information need query select from results
rank using IR model
user:
system:
tf-idf PageRank
bird’s-eye view of how a sear...
Information need query select from results
rank using IR model
user:
system:
tf-idf PageRank
query understanding
4
search is a communication problem
5
6
tag: skill OR title
related skills:
search, ranking, …
tag: company
id: 1337
industry: internet
verticals:
people, jobs
...
query understanding pipeline
7
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured qu...
query understanding pipeline
8
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured qu...
9
fix obvious typos
help users spell names
spelling correction
spelling out the details
10
PEOPLE NAMES
COMPANIES
TITLES
PAST QUERIES
n-grams
marissa => ma ar ri is ss sa
metaphone
mark...
spelling out the details
11
problem: corpus as well as query logs contain many spelling errors
certain spelling errors are...
spelling out the details
12
problem: corpus & query logs contain spelling errors
solution: use query chains to infer corre...
query understanding pipeline
13
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured q...
query tagging: identifying entities in the query
14
TITLE CO GEO
TITLE-237
software engineer
software developer
programmer...
query tagging: identifying entities in the query
15
TITLE CO GEO
MORE PRECISE MATCHING WITH DOCUMENTS
entity-based filtering
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BEFORE
entity-based filtering
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AFTER
BEFORE
entity-based filtering
18
BEFORE
entity-based filtering
19
AFTER
BEFORE
entity-based suggestions
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entity-based suggestions
21
query tagging: sequential model
22
EMISSION PROBABILITIES
(learned from user profiles)
TRANSITION PROBABILITIES
(learned f...
query tagging: sequential model
23
INFERENCE
given a query, find the most likely sequence of tags
query understanding pipeline
24
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured q...
vertical intent prediction: distribution
25
JOBS
PEOPLE
COMPANIES
(probability distribution over verticals)
vertical intent prediction: relevance
26
[company]
[employees]
[jobs]
[name search]
query understanding pipeline
27
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured q...
28
query expansion: name synonyms
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query expansion: job title synonyms
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query expansion: signals
[jon] [jonathan] CLICK
trained using query chains:
[programmer] [developer] CLICK
symmetric bu...
query understanding pipeline
31
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured q...
32
what else can we learn from search in the wild?
don’t guess when it’s better to ask
33
vs.
clarify then refine
34
computers books
give users transparency, guidance, and control
35
think beyond individual search queries
36
Gene Golovchinsky, FXPAL
know when you don’t know
37
Claudia Hauff, Query Difficulty for Digital Libraries [2009]
38
Daniel Tunkelang
dtunkelang@linkedin.com
https://linkedin.com/in/dtunkelang
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search is a communication problem Better Search Through Query Understanding

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search is a communication problem
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  • How is it possible? 80% accuracy? Sports picks directly from the insiders? ♣♣♣ https://tinyurl.com/yxcmgjf5
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  • Thanks for sharing. This is great ... clean and neat preso... one question though . did you use HMM or any specific technique for sequence modelling ? I am looking to implement a query tagger for better understanding our user queries... I am wondering how did you go about creating training set .. was it more of labor intensive human labelled tags or some automated way using dictionaries ? Some context on our biz. we are into selling consumer electronics, home & FMCG product with a catalog size of 1.5M unique products.
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  • Sure without query concept you can't do find any thing.
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