Learning to rank with multi-aspect relevance for vertical search

Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). Thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the trade...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. Tseng
• Keywords: Data Mining, Information Storage And Retrieval, Meaning Of Relevance, Model Aggregation, WSDM 2012