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SIGIR 2013 Recap September 25, 2013.

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Presentation on theme: "SIGIR 2013 Recap September 25, 2013."— 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 Recap

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

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

13 Paper Review by Nadia Vase
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
Features: query-based, time, per-session: query counts, reformulations, click counts, #searches w/ short dwell times, # “quick backs”, action counts 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
Milad Shokouhi (Microsoft) Ryen W. White (Microsoft Research) Paul Bennett (Microsoft Research) Filip Radlinski (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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