Selective Application of Query-Independent Features in Web Information Retrieval

The application of query-independent features, such as PageRank, can boost the retrieval effectiveness of a Web Information Retrieval (IR) system. In some previous works, a query-independent feature is uniformly applied to all queries. Other works predict the most useful feature based on the query type. However, the accuracy of the current query type prediction methods is not high. In this paper, we investigate a novel approach that applies the most appropriate query-independent feature on a per-query basis, and does not require the knowledge of the query type. The approach is based on an estimate of the divergence between the retrieved document scores’ distributions prior to, and after the integration of a query-independent feature. We evaluate our approach on the TREC .GOV Web test collection and the mixed topic sets from TREC 2003 & 2004 Web search tasks. Our experimental results demonstrate that the selective application of a query-independent feature on a per-query basis is very effective and robust. In particular, it outperforms a query type prediction-based method, even when this method is simulated with a 100% query type prediction accuracy.