Contextual Multidimensional Relevance Models

Abstract

Information retrieval systems centrally build upon the concept of relevance in order to rank documents in response to a user's query. Assessing relevance is a non-trivial operation that can be influenced by a multitude of factors that go beyond mere topical overlap with the query. This thesis argues that relevance depends on personal (Chapter 2) and situational (Chapter 3) context. In many use cases, there is no single interpretation of the concept that would optimally satisfy all users in all possible situations. We postulate that relevance should be explicitly modelled as a composite notion comprised of individual relevance dimensions. To this end, we show how automatic inference schemes based on document content and user activity can be used in order to estimate such constituents of relevance (Chapter 4). Alternatively, we can employ human expertise, harnessed, for example, via commercial crowdsourcing or serious games to judge the degree to which a document satisfies a given set of relevance dimensions (Chapter 5). Finally, we need a model that allows us to estimate the joint distribution of relevance across all previously obtained dimensions. In this thesis, we propose using copulas, a model family originating from the field of quantitative finances that decouples observations and dependency structure and which can account for complex non-linear dependencies among relevance dimensions (Chapter 6).