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Ranking using Multiple Document Types in Desktop Search

Ranking using Multiple Document Types in Desktop Search. Jinyoung Kim, W. Bruce Croft SIGIR ’ 10 Speaker: Hsin-Lan, Wang Date: 2011/05/02. Outline. Introduction Retrieval Model Type-specific Retrieval Type Prediction Result Merging Experiment Conclusion. Introduction.

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Ranking using Multiple Document Types in Desktop Search

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  1. Ranking using Multiple Document Types in Desktop Search Jinyoung Kim, W. Bruce Croft SIGIR’10 Speaker: Hsin-Lan, Wang Date: 2011/05/02

  2. Outline • Introduction • Retrieval Model • Type-specific Retrieval • Type Prediction • Result Merging • Experiment • Conclusion

  3. Introduction • People have many types of documents on their desktop with different sets of metadata for each type. • email→sender、receiver • office document→filename、author • Desktop search system: being able to predict which type of document a user is looking for given a query.

  4. Introduction • Main goal: to show how the retrieval effectiveness of a desktop search system can be enhanced by improving type prediction performance.

  5. Retrieval Model • query Q = (q1,…,qm) • each collection C contains documents of n field types (F1,…,Fn) • each document d may include fields (f1,…,fn)

  6. Type-specific Retrieval • Goal: to rank documents from each sub-collection. • Probabilistic Retrieval Model for Semi-structured Data PQL(qi|fj)=(1-λ)P(qi|fj)+λP(qi|Fj)

  7. Type Prediction • Goal: to score each collection given a user query. • Methods for Type Prediction • Query-likelihood of Collection • Query-likelihood of Query Log

  8. Type Prediction • Methods for Type Prediction • Geometric Average • ReDDE • Query Clarity

  9. Type Prediction • Methods for Type Prediction • Dictionary-based Matching • Built the dictionary for each sub-collection by using the names of the collection and metadata fields. • Using Document Metadata Fields • New method: field-based collection query likelihood (FQL).

  10. Type Prediction • Combining Type Prediction Methods • Grid-search of Parameter Values • Golden Section Search • Multi-class Classification • MultiSVM (Liblinear Toolkit) • Rank-learning Method • RankSVM

  11. Result Merging • C: collection score (from type prediction) • D: document score (from type-specific retrieval)

  12. Experiment • Pseudo-desktop Collection • Generation Method • collect documents with similar characteristics • generate queries by statistically taking terms from each of the target documents

  13. Experiment • Pseudo-desktop Collection • Prediction Accuracy • Retrieval Performance • Mean Reciprocal Rank

  14. Experiment • Best: use the retrieval method with the best aggregate performance for each sub-collection. • Uniform: each collection has the same chance of containing the relevant document. • Oracle: have perfect knowledge of the collection that contains the relevant document

  15. Experiment • CS Collection • Generation Method • DocTrack game • Prediction Accuracy

  16. Experiment • CS Collection • Retrieval Performance • Leave-one-out Prediction Accuracy

  17. Conclusion • Suggest a retrieval model for desktop search where type-specific retrieval results are merged into the final rank list based on type prediction scores. • Introduce FQL – a new type prediction method.

  18. Conclusion • Develop a human computation game for collecting queries in a more realistic setting. • Show that the combination method can improve type prediction performance.

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