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SIGIR 2013 Recap

SIGIR 2013 Recap. September 25, 2013. Today’s Paper Summaries. Yu Liu Personalized Ranking Model Adaptation for Web Search Nadia V ase Toward Self-Correcting Search Engines: Using Underperforming Queries to Improve Search Riddick Jiang

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SIGIR 2013 Recap

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  1. SIGIR 2013 Recap September 25, 2013

  2. Today’s Paper Summaries • Yu Liu • Personalized Ranking Model Adaptation for Web Search • Nadia Vase • Toward Self-Correcting Search Engines: Using Underperforming Queries to Improve Search • Riddick Jiang • Fighting Search Engine Amnesia: Reranking Repeated Results SIGIR 2013 Recap

  3. SIGIR 2013 Reference Material • Jul 28 – Aug 1, 2013. Dublin, Ireland • Proceedings (ACM Digital library): http://dl.acm.org/citation.cfm?id=2484028 • Available free via the eBay intranet • Best paper nominations: http://www.bibsonomy.org/user/nattiya/sigir2013 • Papers we liked: SIGIR 2013 Recap Wiki • SIGIR 2014: July 6-11, Queensland, Australia SIGIR 2013 Recap

  4. Personalized Ranking Model Adaptation for Web Search Hongning Wang (University of Illinois at Urbana-Champaign)Xiaodong He (Microsoft Research)Ming-Wei Chang (Microsoft Research)Yang Song (Microsoft Research)Ryen W. White (Microsoft Research)Wei Chu (Microsoft Bing) Paper Review by Yu Liu SIGIR 2013 Recap

  5. Motivations • Searcher’s information needs are diverse • Need personalization for web search • Existing methods for personalization • Extracting user-centric features [Teevan et al. SIGIR’05] • Location, gender, click history • Require large volume of user history • Memory-based personalization [White and Drucker WWW’07, Shen et al. SIGIR’05] • Learn direct association between query and URLs • Limited coverage, poor generalization • Major considerations • Accuracy • Maximize the search utility for each single user • Efficiency • Executable on the scale of all the search engine users • Adapt to the user’s result preferences quickly

  6. Personalized Ranking Model Adaptation Adapting the global ranking model for each individual user Adjusting the generic ranking model’s parameters with respect to each individual user’s ranking preferences

  7. Linear Regression Based Model Adaptation Lose function from any linear learning-to-rank algorithm, e.g., RankNet, LambdaRank, RankSVM Complexity of adaptation • Adapting global ranking model for each individual user SIGIR 2013 @ Dublin Ireland

  8. Ranking feature grouping • Organize the ranking features so that shared transformation is performed on the parameters of features in the same group • Maps V original ranking features to K different groups • Grouping features by name - Name • Exploring informative naming scheme • BM25_Body, BM25_Title • Clustering by manually crafted patterns • Co-clustering of documents and features – SVD [Dhillon KDD’01] • SVD on document-feature matrix • k-Means clustering to group features • Clustering features by importance - Cross • Estimate linear ranking model on different splits of data • k-Means clustering by feature weights in different splits

  9. Discussion • A general framework for ranking model adaptation • Applicable to a majority of existing learning-to-rank algorithms • Model-based adaptation, no need to operate on the numerous data from the source domain • Within the same optimization complexity as the original ranking model • Adaptation sharing across features to reduce the requirement of adaptation data

  10. Experimental Setup • Dataset • Bing.com query log: May 27, 2012 – May 31, 2012 • Manual relevance annotation • 5-grade relevance score • 1830 ranking features • BM25, PageRank, tf*idf and etc. SIGIR 2013 @ Dublin Ireland

  11. Improvement analysis • User-level improvement • Against global model SIGIR 2013 @ Dublin Ireland

  12. Conclusions • Efficient ranking model adaption framework for personalized search • Linear transformation for model-based adaptation • Transformation sharing within a group-wise manner • Future work • Joint estimation of feature grouping and model transformation • Incorporate user-specific features and profiles • Extend to non-linear models SIGIR 2013 @ Dublin Ireland

  13. TOWARD SELF-CORRECTING SEARCH ENGINES:USING UNDERPERFORMING QUERIES TO IMPROVE SEARCH Ahmed Hassan (Microsoft)Ryen W. White (Microsoft Research)Yi-Min Wang (Microsoft Research) Paper Review by Nadia Vase SIGIR 2013 Recap

  14. Overview • What to do with a dissatisfying query? • Why is it bad? New features to fix it? • If the same problem recurs, can find a pattern • Identify dissatisfying (DSAT) queries • Cluster them • Train specialized rankers+general ranker SIGIR 2013 Recap

  15. Identifying dissatisfying queries • Use toolbar data • Based on search engine switching events • 60% of switching events: DSAT search • Trained classifier to predict switch cause • Logistic regression, 562 labeled, 107 users • Binary classifier SIGIR 2013 Recap

  16. Features for dissatisfying switches SIGIR 2013 Recap

  17. Clustering DSAT Queries • What to do with DSAT queries • DSAT instance has 140 binary features • Query: length, language, “phrase (NP, VP) type”, ODP category • SERP: direct answer/feature, query suggestion shown, spell correction, etc • Search instance: market (US, UK, etc), query vertical (Web, News, etc), search engine, temporal attributes • Use Weka’s implementation of FP-Growth to cluster SIGIR 2013 Recap

  18. Clustering: FP-Growth filter and order features &create the FP-tree bottom-up algorithm to find attribute clusters SIGIR 2013 Recap

  19. Example of attribute sets SIGIR 2013 Recap

  20. Building Modified Rankers • 2nd round ranker per each DSAT group • Trained DSAT data, general ranker’s output score SIGIR 2013 Recap

  21. Experiment results SIGIR 2013 Recap

  22. Fighting Search Engine Amnesia: Reranking Repeated Results MiladShokouhi(Microsoft)Ryen W. White (Microsoft Research)Paul Bennett (Microsoft Research)FilipRadlinski(Microsoft) Paper Review by Riddick Jiang SIGIR 2013 Recap

  23. Repetition 40%-60% sessions have two queries or more 16- 44% of sessions (depending on the search engine) with two queries have at least one repeated result Repetition increases to almost all sessions with ten or more queries SIGIR 2013 Recap

  24. Intuition • Promote new results (previously missed or new) • Demote previously skipped results • Demote previously clicked results • Promote previously clicked results if clicked >= 2 (personal nav) SIGIR 2013 Recap

  25. SIGIR 2013 Recap

  26. CTR for skipped results SIGIR 2013 Recap

  27. CTR for clicked results SIGIR 2013 Recap

  28. Ranking features SIGIR 2013 Recap

  29. Evaluation Personal Nav: Score, Position, and a Personal Navigation feature- counts the number of times a particular result has been clicked for the same query previously in the session ClickHistory: Score, Position, and Click-history - click counts for each result on a per query basis SIGIR 2013 Recap

  30. A/B testing Interleave results from R-cube and control randomly allocating each result position to R-cube or the baseline Credit click to the corresponding ranker Five days in June, 2012 370,000 queries R-cube ranker was preferred for 53.8% of queries statistically significant SIGIR 2013 Recap

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