Learning to select a ranking function

TL;DR: This paper proposes a novel Learning To Select framework that selectively applies an appropriate ranking function on a per-query basis, and proposes 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.

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 using tasks from the TREC Web and Million Query tracks, in combination with the LETOR 3.0 and LETOR 4.0 feature sets. Our experimental results show that our proposed method is effective and robust for selecting an appropriate ranking function on a per-query basis. In particular, it always outperforms three state-of-the-art LTR techniques, namely Ranking SVM, AdaRank, and the automatic feature selection method.