Learning to Rank using Query-Level Rules - Veloso, Adriano; Gonçalves, Marcos A.; Meira Jr., Wagner; Mossri, Humberto (Journal of Information and Data Management, 2010)
Most existing learning to rank methods neglect query-sensitive information while producing functions to estimate the relevance of documents (i.e., all examples in the training data are treated indistinctly, no matter the query associated with them). This is counter-intuitive, since the relevance of a document depends on the query context (i.e., the same document may have different relevances, depending on the query associated with it). In this paper we show that query-sensitive information is of paramount importance for improving ranking performance. We present novel learning to rank methods. These methods use rules associating document features to relevance levels as building blocks to produce ranking functions. Such rules may have different scopes: global rules (which do not exploit query-sensitive information) and query-level rules. Firstly, we discuss a basic method, RE-GR (Relevance Estimation using Global Rules), which neglects any query-sensitive information, and uses global rules to produce a single ranking function. Then, we propose methods that effectively exploit query-sensitive information in order to improve ranking performance. The RE-SR method (Relevance Estimation using Stable Rules), produces a single ranking function using stable rules, which are rules carrying (almost) the same information no matter the query context. The RE-QR method (Relevance Estimation using Query-level Rules), is much finer-grained. It uses query-level rules to produce multiple query-level functions. The estimates provided by such query-level functions are combined according to the competence of each function (i.e., a measure of how close the estimate provided by a query-level function is to the true relevance of the document). We conducted a systematic empirical evaluation using the LETOR 4.0 benchmark collections. We show that the proposed methods outperform state-of-the-art learning to rank methods in most of the subsets, with gains ranging from 2% to 9%. We further show that RE-SR and RE-QR, which use query-sensitive information while producing ranking functions, achieve superior ranking performance when compared to RE-GR.