Query-Dependent Ranking for Web Information Retrieval

Abstract

In information retrieval, the users’ queries often vary a lot from one to another. Most IR systems use a single fixed ranking strategy to support the information seeking task of all users for all queries irrespective of the heterogeneity of end users and queries. The main problem for this work is that no single ranking strategy performs the best for all queries. This work considers query difference in developing ranking function by clustering the query. Then the cluster membership information is combined into the learning process of the ranking function in order to combine the ranking risks of all training examples with different weights according to the training query’s similarity to different query cluster for ranking model construction. To verify the benefit of the proposed querydependent ranking system experiments were conducted on TREC 2003 and TREC 2004 datasets in LETOR (Learning To Rank) package. The ranking accuracy of the system is evaluated by Normalized discount cumulative gain (NDCG)evaluation metric.