1. Using Statistical Decision Theory and Relevance Models for Query-Performance Prediction Anna Shtok and Oren Kurland and David Carmel SIGIR 2010 Hao-Chin Chang Department of Computer Science & Information Engineering National Taiwan Normal University 2011/08/01

2. 2 Outline Introduction Relevance-Model Relevance Score –Clarity –WIG –NUC –QF Ranking List Experiment Conclusion

3. Introduction We present a novel framework for query-performance prediction that is based on statistical decision theory and relevance model. We consider a ranking induced by a retrieval method in response to a query as a decision taken so as to satisfy the underlying information need. Our goal is to predict the query-performance of M with respect to q. We instantiate various query-performance predictors from the framework by varying the –estimates of the relevance-model –measures for the quality of a relevance-model estimate –selects a measure of similarity between ranked lists 3

4. Relevance-Model represents the information need I q Negative Cross Entropy 4

5. Relevance Score(Clarity,WIG) The socre be measured by the KL divergence WIG is based on estimating the presumed percentage of relevant documents in the set S from which is constructed 5

6. Relevance Score(NQC) NQC, is based on the hypothesis that the standard deviation of retrieval scores in the result list is negatively correlated with the potential amount of query drift — i.e., non-query-related information manifested in the list. u is the mean retrieval score in 6

7. Relevance Score(QF)  this goal is to represent ranked list L by a language model  Terms are ranked by their contribution to the language model’s KL (Kullback-Leibler) divergence from the background collection model.  Top ranked terms will be chosen to form the new query Q’ 7

8. Relevance Score(QF) P(D|L) is estimated by a linearly decreasing function of the rank of document D Each term in P(w|L) is ranked The top N ranked terms by form a weighted query Q={(w i,t i )} w i denotes the i-th ranked term weight t i is the KL-divergence contribution of w i 8

9. Similarity between ranked lists Pearson’s coefficient and Spearman’s-ρ and Kendall’s-γ correlation between the original list ranking and its relevance model based ranking are computed 9

10. Experiment 10

11. Experiment 11

12. Experiment 12

13. 13

14. 14 Conclusion Improving the sampling technique used for relevance model construction Devising and adapting better measures of representativeness for relevance models constructed form cluster