Paper Info
Title
Flexible pseudo-relevance feedback via selective sampling
Abstract
Although Pseudo-Relevance Feedback (PRF) is a widely used technique for enhancing average retrieval performance, it may actually hurt performance for around one-third of a given set of topics. To enhance the reliability of PRF, Flexible PRF has been proposed, which adjusts the number of pseudo-relevant documents and/or the number of expansion terms for each topic. This paper explores a new, inexpensive Flexible PRF method, called Selective Sampling, which is unique in that it can skip documents in the initial ranked output to look for more “novel” pseudo-relevant documents. While Selective Sampling is only comparable to Traditional PRF in terms of average performance and reliability, per-topic analyses show that Selective Sampling outperforms Traditional PRF almost as often as Traditional PRF outperforms Selective Sampling. Thus, treating the top P documents as relevant is often not the best strategy. However, predicting when Selective Sampling outperforms Traditional PRF appears to be as difficult as predicting when a PRF method fails. For example, our per-topic analyses show that even the proportion of truly relevant documents in the pseudo-relevant set is not necessarily a good performance predictor.
Year
DOI
Venue
2005
10.1145/1105696.1105699
ACM Trans. Asian Lang. Inf. Process.
Keywords
Field
DocType
good performance predictor,traditional prf,selective sampling,flexible prf,inexpensive flexible prf method,pseudo-relevant document,average performance,prf method,pseudo-relevance feedback,per-topic analysis,flexible pseudo-relevance feedback,average retrieval performance
Relevance feedback,Ranking,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Sampling (statistics),Machine learning
Journal
Volume
Issue
Citations 
4
2
43
PageRank 
References 
Authors
1.72
31
3
Name
Order
Citations
PageRank
Tetsuya Sakai11460139.97
Toshihiko Manabe2908.90
Makoto Koyama3838.49