Search behavior-driven training for result re-ranking

In this paper we present a framework for improving the ranking learning process, taking into account the implicit search behaviors of users. Our approach is query-centric. That is, it examines the search behaviors induced by queries and groups together queries with similar such behaviors, forming search behavior clusters. Then, it trains multiple ranking functions, each one corresponding to one of these clusters. The trained models are finally combined to re-rank the results of each new query, taking into account the similarity of the query with each cluster. The main idea is that similar search behaviors can be detected and exploited for result re-ranking by analysing results into feature vectors, and clustering them. The experimental evaluation shows that our method improves the ranking quality of a state of the art ranking model.