Context Recommendation Using Multi-label Classification

Context-aware recommender systems (CARS) are extensions of traditional recommenders that also take into account contextual condition of a user to whom a recommendation is made. The recommendation problem is, however, still focused on recommending a set of items to a target user. In this paper, we consider the problem of recommending to a user the appropriate contexts in which an item should be selected. We believe that context recommenders can be used as another set of tools to assist users´ decision making. We formulate the context recommendation problem and discuss the motivation behind and possible applications of the concept. We identify two general classes of algorithms to solve this problem: direct context prediction and indirect context recommendation. Furthermore, we present and evaluate several direct context prediction algorithms based on multi-label classification (MLC). Our experiments demonstrate that the proposed approaches outperform the baseline methods, and also that personalization is required to enhance the effectiveness of context recommenders.