Flexible Pseudo-Relevance Feedback via Selective Sampling

摘要

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.