Semantic contextualisation in a news recommender system

Abstract

The elements that can be considered under the notion of context in a recommender system are manifold: user tasks/goals, recently browsed/rated items, computing platforms and network conditions, social environment, physical environment and location, time, external events, etc. Complementarily to these elements, we propose a particular notion of context for semantic content retrieval: that of semantic runtime context, which we define as the background topics under which activities of a user occur within a given unit of time. A runtime context is represented in our approach as a set of weighted concepts from domain ontologies, obtained by collecting the concepts that have been involved in user’s actions (e.g., accessed items) during a session. Once the context is built, a contextual activation of user preferences is achieved by finding semantic paths linking preferences to context. In this paper, we present a user-centred study of our context-aware recommendation model using a news recommender system called News@hand. We analyse the strengths and weaknesses of our approach, and discuss the importance of contextualisation in a news recommendation scenario.