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Recruiting Solutions query understanding

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Daniel Tunkelang
Daniel Tunkelang

This document discusses query understanding in search engines. It describes how query understanding involves identifying entities and tags in queries, predicting the user's intent or topic area, expanding queries using related terms, and incorporating spelling corrections. The key aspects of query understanding covered are tagging queries for entities like names, titles, companies; predicting the user's vertical intent like jobs, people or companies; and expanding queries using name synonyms, job title synonyms or signals from past user queries and clicks. The document also suggests giving users more transparency, guidance and control over the search process.

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Recruiting SolutionsRecruiting SolutionsRecruiting Solutions Daniel Tunkelang Head, Query Understanding better search through query understanding
overview  query understanding: what is it?  how we do query understanding at LinkedIn  some other thoughts from search in the wild what I’m not going to cover: 2
Information need query select from results rank using IR model user: system: tf-idf PageRank bird’s-eye view of how a search engine works 3
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 intent: exploratory
query understanding pipeline 7 spellcheck query tagging vertical intent prediction query expansion raw query structured query + annotations
query understanding pipeline 8 spellcheck query tagging vertical intent prediction query expansion raw query structured query + annotations
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/marc => MRK co-occurrence counts marissa:mayer = 1000 marisa meyer yahoo marissa marisa meyer mayer yahoo
spelling out the details 11 problem: corpus as well as query logs contain many spelling errors certain spelling errors are quite frequent while genuine words (especially names) might be infrequent
spelling out the details 12 problem: corpus & query logs contain spelling errors solution: use query chains to infer correct spelling [product manger] [product manager] CLICK [marissa mayer] CLICK
query understanding pipeline 13 spellcheck query tagging vertical intent prediction query expansion raw query structured query + annotations
query tagging: identifying entities in the query 14 TITLE CO GEO TITLE-237 software engineer software developer programmer … CO-1441 Google Inc. Industry: Internet GEO-7583 Country: US Lat: 42.3482 N Long: 75.1890 W (RECOGNIZED TAGS: NAME, TITLE, COMPANY, SCHOOL, GEO, SKILL )
query tagging: identifying entities in the query 15 TITLE CO GEO MORE PRECISE MATCHING WITH DOCUMENTS
entity-based filtering 16 BEFORE
entity-based filtering 17 AFTER BEFORE
entity-based filtering 18 BEFORE
entity-based filtering 19 AFTER BEFORE
entity-based suggestions 20
entity-based suggestions 21
query tagging: sequential model 22 EMISSION PROBABILITIES (learned from user profiles) TRANSITION PROBABILITIES (learned from query logs) TRAINING
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 query + annotations
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 query + annotations
28 query expansion: name synonyms
29 query expansion: job title synonyms
30 query expansion: signals [jon] [jonathan] CLICK trained using query chains: [programmer] [developer] CLICK symmetric but not transitive! [francis] ⇔ [frank] [franklin] ⇔ [frank] [francis] ≠ [franklin] [software engineer] [software developer] CLICK context based! [software engineer] => [software developer] [civil engineer] ≠ [civil developer]
query understanding pipeline 31 spellcheck query tagging vertical intent prediction query expansion raw query structured query + annotations
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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