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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-ltering technique based on implicit user feedback. We introduce a new context-aware recommendation approach called user micro-proling . We split each single user prole into several possibly overlapping sub-proles, each representing users in particular contexts. The predictions are done using these micro-proles instead of a single user model. The users’ taste can depend on the exact partition of the contextual variable. The identication of a meaningful partition of the users’ prole and its evaluation is a non-trivial task, especially when using implicit feedback and a continuous contextual domain. We propose an o-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem.