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

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

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 = 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- Completion Z. 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 hyderabad hyderabad airport hyderabad history hyderabad maps hyderabad india hyderabad hotels hyderabad www Current Desired

16. 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 MostPopular Completion

17. Nearest Completion www 2011 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) hyderabad airport hyderabad maps hyderabad india hydroxycuthyperbola hyundai hyatt

18. Nearest Completion: Framework Nearest Neighbors Search Nearest Neighbors Search context candidate completions Repository top k context- related completions offline 1.Expand completions 2.Index completions online 1. Expand context query 2. Search for similar completions 3. Return top k 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 contextqueryMostPopularNearestHybrid french flagitalian flaginternet im help irs ikea internet explorer italian flag itunes and french ireland italy irealand internet italian flag itunes and french im help irs neptuneuranusups usps united airlines usbank used cars uranus uranas university university of chic… ultrasound uranus uranas ups united airlines usps improving acer laptop battery bank of america bank of america bankofamerica best buy bed bath and b… battery powered … battery plus cha… bank of america best buy battery powered …