Learning to Select a Ranking Function

Abstract. Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. In this paper, we propose a novel Learning To Select framework that selectively applies an appropriate ranking function on a per-query basis. The approach employs a query feature to identify similar training queries for an unseen query. The ranking function which performs the best on this identified training query set is then chosen for the unseen query. In particular, we propose the use of divergence, which measures the extent that a document ranking function alters the scores of an initial ranking of documents for a given query, as a query feature. We evaluate our method...

• Type: Conference
• Year: 2010
• Where: ECIR
• Authors: Jie Peng, Craig Macdonald, Iadh Ounis
• Keywords: Appropriate Ranking Function, Document Ranking Function, ECIR 2010, Information Technology, Ranking Function