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Published on November 21, 2007

Author: Brainy007

Source: authorstream.com

Context-Sensitive IR using Implicit Feedback :  Context-Sensitive IR using Implicit Feedback Xuehua Shen, Bin Tan, ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign Problem of Context-Independent Search:  Problem of Context-Independent Search Jaguar Put Search in Context:  Other Context Info: Dwelling time Mouse movement Put Search in Context Apple software Hobby … Problem Definition:  Problem Definition … How to model and use all the information? e.g., Apple software e.g., Apple - Mac OS X The Apple Mac OS X product page. Describes features in the current version of Mac OS X, a screenshot gallery, latest software downloads, and a directory of ... Outline:  Outline Four contextual statistical language models Experiment design and results Summary and future work Retrieval Model:  Retrieval Model Qk D θQk θD Similarity Measure Results Basis: Unigram language model + KL divergence Fixed Coefficient Interpolation (FixInt):  Fixed Coefficient Interpolation (FixInt) Qk Bayesian Interpolation (BayesInt):  Bayesian Interpolation (BayesInt) Intuition: if the current query Qk is longer, we should trust Qk more Online Bayesian Update (OnlineUp):  Online Bayesian Update (OnlineUp) Intuition: continuous belief update about user information need Batch Bayesian Update (BatchUp):  Batch Bayesian Update (BatchUp) C1 C2 Intuition: clickthrough data may not decay Data Set of Evaluation:  Data Set of Evaluation Data collection: TREC AP88-90 Topics: 30 hard topics of TREC topics 1-150 System: search engine + RDBMS Context: Query and clickthrough history of 3 participants. Experiment Design:  Experiment Design Models: FixInt, BayesInt, OnlineUp and BatchUp Performance Comparison: Qk vs. Qk+HQ+HC Evaluation Metrics: MAP and Pr@20 docs Overall Effect of Search Context:  Overall Effect of Search Context Interaction history helps system improve retrieval accuracy BayesInt better than FixInt; BatchUp better than OnlineUp Using Clickthrough Data Only:  Using Clickthrough Data Only BayesInt (=0.0,=5.0) Sensitivity of BatchUp Parameters:  Sensitivity of BatchUp Parameters BatchUp is stable with different parameter settings Best performance is achieved when =2.0; =15.0 Summary:  Summary Propose four contextual language models to exploit user interaction history for contextual search Construct an evaluation dataset based on TREC data (http://sifaka.cs.uiuc.edu/ir/ucair/QCHistory.zip) Experiment results show that user interaction history, especially clickthrough data, can improve the retrieval accuracy Future Work:  Future Work Study a general framework for interactive information retrieval Study more sophisticated models to incorporate context information Build a system on the client side to capture and exploit user context information Slide18:  Thank you ! The End

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