Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel contextaware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.

In order to generate relevant recommendations, a context-aware recommender system (CARS) not only makes use of user preferences, but also exploits information about the specific contextual situation in which the recommended item will be consumed. For instance, when recommending a holiday destination, a CARS could take into account whether the trip will happen in summer or winter. It is unclear, however, which contextual factors are important and to which degree they influence user ratings. A large amount of data and complex context-aware predictive models must be exploited to understand these relationships. In this paper, we take a new approach for assessing and modeling the relationship between contextual factors and item ratings. Rather than using the traditional approach to data collection, where recommendations are rated with respect to real situations as participants go about their lives as normal, we simulate contextual situations to more easily capture data regarding how the context influences user ratings. To this end, we have designed a methodology whereby users are asked to judge whether a contextual factor (e.g., season) influences the rating given a certain contextual condition (e.g., season is summer). Based on the analyses of these data, we built a contextaware mobile recommender system that utilizes the contextual factors shown to be important. In a subsequent user evaluation, this system was preferred to a similar variant that did not exploit contextual information.keywords Context Á Collaborative filtering Á Recommender system Á Mobile applications Á User study 1 Introduction

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Abstract. Contextual knowledge has been traditionally used in Recommender Systems (RSs) to improve the recommendation accuracy of the core recommendation algorithm. Beyond this advantage, in this paper we argue that there is an additional benefit of context management; making more convincing recommendations because the system can use the contextual situation of the user to explain why an item has been recommended, i.e., the RS can pinpoint the relationships between the contextual situation and the recommended items to justify the suggestions. The results of a user study indicate that context management and this type of explanations increase the user satisfaction with the recommender system.