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SIGIR 2013 Recap September 25, 2013. Today’s Paper Summaries Yu Liu – Personalized Ranking Model Adaptation for Web Search Nadia Vase – Toward Self-Correcting.

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Presentation on theme: "SIGIR 2013 Recap September 25, 2013. Today’s Paper Summaries Yu Liu – Personalized Ranking Model Adaptation for Web Search Nadia Vase – Toward Self-Correcting."— Presentation transcript:

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 Recap2

3 SIGIR 2013 Reference Material Jul 28 – Aug 1, Dublin, Ireland Proceedings (ACM Digital library): – Available free via the eBay intranet Best paper nominations: Papers we liked: SIGIR 2013 Recap WikiSIGIR 2013 Recap Wiki SIGIR 2014: July 6-11, Queensland, Australia SIGIR 2013 Recap3

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) SIGIR 2013 Recap4 Paper Review by Yu Liu

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 Adapting global ranking model for each individual user SIGIR Dublin Ireland7 Lose function from any linear learning-to-rank algorithm, e.g., RankNet, LambdaRank, RankSVM Complexity of adaptation

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 Dublin Ireland10

11 Improvement analysis User-level improvement – Against global model SIGIR Dublin Ireland11

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 Dublin Ireland12

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) SIGIR 2013 Recap13 Paper Review by Nadia Vase

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 Recap14

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 Recap15

16 Features for dissatisfying switches SIGIR 2013 Recap16

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 Recap17

18 Clustering: FP-Growth filter and order features &create the FP-tree bottom-up algorithm to find attribute clusters SIGIR 2013 Recap18

19 Example of attribute sets SIGIR 2013 Recap19

20 Building Modified Rankers 2nd round ranker per each DSAT group – Trained DSAT data, general ranker’s output score SIGIR 2013 Recap20

21 Experiment results SIGIR 2013 Recap21

22 FIGHTING SEARCH ENGINE AMNESIA: RERANKING REPEATED RESULTS Milad Shokouhi (Microsoft) Ryen W. White (Microsoft Research) Paul Bennett (Microsoft Research) Filip Radlinski (Microsoft) SIGIR 2013 Recap22 Paper Review by Riddick Jiang

23 Repetition 40%-60% sessions have two queries or more % 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 Recap23

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 Recap24

25 SIGIR 2013 Recap25

26 CTR for skipped results SIGIR 2013 Recap26

27 CTR for clicked results SIGIR 2013 Recap27

28 Ranking features SIGIR 2013 Recap28

29 Evaluation SIGIR 2013 Recap29 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

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, ,000 queries R-cube ranker was preferred for 53.8% of queries statistically significant SIGIR 2013 Recap30


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