Learning to rank at query-time using association rules

Some applications have to present their results in the form of ranked lists. This is the case of many information retrieval applications, in which documents must be sorted according to their relevance to a given query. This has led the interest of the information retrieval community in methods that automatically learn effective ranking functions. In this paper we propose a novel method which uncovers patterns (or rules) in the training data associating features of the document with its relevance to the query, and then uses the discovered rules to rank documents. To address typical problems that are inherent to the utilization of association rules (such as missing rules and rule explosion), the proposed method generates rules on a demand-driven basis, at query-time. The result is an extremely fast and effective ranking method. We conducted a systematic evaluation of the proposed method using the LETOR benchmark collections. We show that generating rules on a demand-driven basis can boo...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Adriano Veloso, Humberto Mossri de Almeida, Marcos André Gonçalves, Wagner Meira Jr.
• Keywords: Demand-driven Basis, Effective Ranking, Information Retrieval, Information Technology, SIGIR 2008