Towards Time-Dependant Recommendation based on Implicit Feedback

Context-aware recommender systems (CARS) aim at improving users’ satisfaction by tailoring recommendations to each particular context. In this work we propose a contextual pre-filtering technique based on implicit user feedback. We introduce a new context-aware recommendation approach called user micro-profiling. We split each single user profile into several possibly overlapping sub-profiles, each representing users in particular contexts. The predictions are done using these micro-profiles instead of a single user model. The users’ taste can depend on the exact partition of the contextual variable. The identification of a meaningful partition of the users’ profile and its evaluation is a non-trivial task, especially when using implicit feedback and a continuous contextual domain. We propose an off-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem.