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Context-Sensitive Query Auto-Completion

Context-Sensitive Query Auto-Completion. Authors:Naama Kraus and Ziv Bar- Yossef Date of publication:November 2010 speaker:Rishu Gupta. Motivating Example. Desired Result. I want to buy a good Digital Camera. Current Result. digital camera reviews d igital camera buying guide

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Context-Sensitive Query Auto-Completion

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  1. Context-Sensitive QueryAuto-Completion Authors:NaamaKraus and ZivBar-Yossef Date of publication:November 2010 speaker:Rishu Gupta

  2. Motivating Example Desired Result I want to buy a good Digital Camera Current Result digital camera reviews digital camera buying guide digital camera with wifi digital camera deals digital camera world digital picture frame digital copy

  3. Most Challenging Auto-Completion Scenario • Challenge :Query Auto-Completion predicts the correct user’s query with only 12.8% probability. • Goal :To predict the user’s intended query reliably when user has entered only one character. • Advantages: • Makes search experience faster • Reduces load on servers in Instant Search

  4. QAC Algorithms Completion “c” of Top K Completion List QAC Algorithm should also work if “c” is semantically equal to “q” Hash Table or Trie Ordered By Quality Score

  5. Context-Sensitive Auto-Completion • How to Compensate for the lack of information ?? • Observation: • User searches within some context. • User context reflects user’s intent. Context examples • Recent queries • Recently visited pages • Recent Tweets • etc….. Our focus – “Recent queries” • Accessible by search engines • 49% of searches are preceded by a different query in the same session • For simplicity, in this presentation we focus on the most recent query

  6. Recent Query Use Approaches How to tackle this problem ??? Cluster Similar Queries (Use of Techniques like HMMs) Generalize Most Popular Completion Algorithm Nearest Completion Algorithm (Assumption:Context relevant to the query) Problem with this approach ?? • None of these previous studies took the user input (prefix) into account in the prediction • In 37% of the query pairs the former query has not occurred in the log before

  7. Nearest Completion:Measure of Similarity

  8. Evaluation Evaluation metric Evaluation framework

  9. Analysis

  10. Most Popular VS Nearest Completion Relevant Context:MRRof NearestCompletion (with depth-3 traversal) is higher in 48% than that of MostPopular-Completion. NearestCompletion becomes destructive, so its MRR is 19% lower than that of MostPopularCompletion.

  11. How Hybrid Completion Works??

  12. MostPopular, Nearest, and Hybrid (2) HybridCompletion is shown to be at least as good as NearestCompletion when the context is relevant and almost as good as MostPopularCompletion when the context is irrelevant.

  13. Examples

  14. Conclusion Query Auto Completion HybridCompletion Algorithm Context Sensitive-Query Auto Completion MostPopularCompletion Algorithm Nearest Completion Algorithm Convex Combination of NearestCompletion and MostPopular Based on Popular Queries(AOL Query Log) • ReleventContext:Based on Users Recent Queries • Recommendation Based Algorithm: Rich Query Representatin

  15. Future • NearestCompletition: More effective session segmentation technique • Predicting the first query in a session still remains an open problem • Use of Other Context Resources like Recently Visited Web Pages or Search History • Measure of Quality Evaluation should be more relaxed • Rich query representation may be further fine tuned.

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