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Contextual IR

Contextual IR. Naama Kraus. Slides are based on the papers: Searching with Context, Kraft, Chang, Maghoul , Kumar Context-Sensitive Query Auto-Completion, Bar- Yossef and Kraus. The Problem (recap). User queries are an imperfect description of their information needs Examples:.

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Contextual IR

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  1. Contextual IR Naama Kraus Slides are based on the papers: Searching with Context, Kraft, Chang, Maghoul, Kumar Context-Sensitive Query Auto-Completion, Bar-Yossef and Kraus

  2. The Problem (recap) • User queries are an imperfect description of their information needs • Examples: Ambiguous queries: jaguar General queries: haifa Terminology differences (synonyms) between user and corpus stars - planets

  3. Contextual IR • Leverage context to better understand the user’s information need • Context types • Short-term context • Current time and location, recent queries, recent page visits, current page viewed, recent tweets, recent e-mails … • Long-term context (user profile/model) • Long-term search history, user interests, user demographics (gender, education…), emails, desktop files… Today’s focus: short-term context

  4. Example Document retrieval – use context to disambiguate queries jaguar recently viewed page

  5. Searching with Context Kraft, Chang, Maghoul, Kumar, WWW’06

  6. Searching with Context • Goal: improve document retrieval • Capture user’s recent context • Piece of text • Extract terms from a page a user is currently viewing, a file a user is currently editing … • Proposes three different methods • Query rewriting (QR) • Add terms to the user’s original query • Rank biasing (RB) • Re-rank results • Iterative filtering meta-search (IFM) • Generate sub-queries and aggregate results

  7. Query Rewriting • Send one simple query to a standard search engine • Augment top context terms to original query • AND semantics • Parameter: how many terms to add • Query q • Context term weighted vector (a b c d e) • Terms are ranked by their weight • Q_new = (q a b) for parameter 2

  8. Rank-Biasing • Send complex query that contains ranking instructions to the search engine • Does not change the original result set, only the ranking • <q> = <selection=cat> <optional=persian,2.0> • Selection terms – original query terms • Optional terms – context terms • boost is a function of their weight new query definition must appear terms optional terms with boost factor (influence on ranking)

  9. Iterative Filtering Meta-Search • Intuition: “explore” different ways to express an information need • Algorithm outline • Generate sub-queries • Send to search engine • Aggregate results

  10. Sub-query Generation • Use a query template • Example: • Query q ; context = (a, b ,c) • Sub-queries • q a , q b , q c • q a b , q b c • q a b c

  11. Ranking and Filtering • Issue k sub-queries to standard SE • Obtain results • Challenge – how to combine, rank and filter results ? • Use rank aggregation techniques

  12. Rank Averaging • A rank aggregation method (out of many…) • Given: k lists of top results • Assign score to each position in the list • E.g., 1 to first position, 2 to second position … • For each document, average over its scores in the k lists • The final list is constructed using the average scores

  13. Context-Sensitive Query Auto-CompletionZ. Bar-Yossef and N. Kraus, WWW’11

  14. Query Auto-Completion An integral part of the user’s search experience Use Cases Predict the user’s intended query Save her key strokes Assist a user to formulate her information need

  15. Motivating Example I am attending WWW 2011 I need some information about Hyderabad Desired Current hyderabad hyderabad airport hyderabad history hyderabad maps hyderabadindia hyderabad hotels hyderabad www

  16. MostPopular Completion MostPopular is not always good enough User queries follow a power law distribution A heavy tail of unpopular queries MostPopular is likely to mis-predict when given a small number of keystrokes

  17. Nearest Completion hyderabad hyderabad maps hyderabad airport hyatt www 2011 hyderabad india hyundai hydroxycut hyperbola Idea: leverage recent query context Intuition: the user’s intended query is similar to her context query  need a similarity measure between queries (refer to paper)

  18. Nearest Completion: Framework online 1. Expand context query 2. Search for similar completions 3. Return top k completions offline Expand completions Index completions context Nearest Neighbors Search candidate completions Repository top k context- related completions

  19. HybridCompletion Problem • If context queries are irrelevant to current query, NearestCompletion fails to predict user’s query. Solution • HybridCompletion: a combination of highly popular and highly context-similar completions • Completions that are both popular and context-similar get promoted • hybscore(q) = c Zsimscore(q) + (1-c) Zpopscore(q) , c [0,1] • Convex combination

  20. MostPopular, Nearest, and Hybrid (1)

  21. MostPopular, Nearest, and Hybrid (2)

  22. Anecdotal Examples

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