Toward a Personalized Approach for Combining Document Relevance Estimates

A large body of work in the information retrieval area has highlighted that relevance is a complex and a challenging concept. The underlying complexity appears mainly from the fact that relevance is estimated by considering multiple dimensions and that most of them are subjective since they are user-dependent. While the most used dimension is topicality, recent works risen particularly from personalized information retrieval have shown that personal preferences and contextual factors such as interests, location and task peculiarities have to be jointly considered in order to enhance the computation of document relevance. To answer this challenge, the commonly used approaches are based on linear combination schemes that rely basically on the non-realistic independency property of the relevance dimensions. In this paper, we propose a novel fuzzy-based document relevance aggregation operator able to capture the user’s importance of relevance dimensions as well as information about their interaction. Our approach is empirically evaluated and relies on the standard TREC contextual suggestion dataset involving 635 users and 50 contexts. The results highlight that accounting jointly for individual differences toward relevance dimension importance as well as their interaction introduces a significant improvement in the retrieval performance.