Metric Learning For Context-Aware Recommender Systems

Abstract

Context-Aware Recommender Systems (CARS) refer to recommender systems that can incorporate side information regarding to users, items and ratings. In the present study, we are concerned with CARS, where the side information is provided in the form of item-attribute matrix with entries indicating whether an item has an attribute. We propose to multiply this matrix with user-item rating matrix to represent the the users in the attribute space of the items. We then apply a popular metric learning method, specifically Mahalanobis Metric Learning (MMC), in the attribute space to calculate the distances between the users and their favorite items as less as possible. We recommend the n items that are closest to the users based on these calculations. We verify the effectiveness of the proposed method on two famous MovieLens datasets that differ in size showing that using metric learning increases the success of CARS up to 7% in comparison with using the traditional cosine similarity.