Building Rich User Search Queries Profiles

Abstract

Abstract. It is well-known that for a variety of search tasks involving queries more relevant results can be presented if they are personalized according to a user's interests and search behavior. This can be achieved with user-dependent, rich web search queries profiles. These are typically built as part of a specific search personalization task so that it is unclear which characteristics of queries are most effective for modeling the user-query relationship in general. In this paper, we explore various approaches for explicitly modeling this user-query relationship independently of other search components. Our models employ generative models in layers in a prediction task. The results show that the best signals for modeling the user-query relationship come from the given query's terms and entities together with information from related entities and terms, yielding a relative improvement of up to 24.5% in MRR and Success over the baseline methods.