Increasing recommendation accuracy and diversity via social networks hyperbolic embedding

Abstract

Several applications are built around sharing information by leveraging social network connections. For example, in social buying sites like Groupon, a deal is usually forwarded to interested recipients through their social graph. A primary goal is to improve user satisfaction by maximizing the relevance of the shared message to the target audience. In order to suggest more personalized products, one should consider offering not only accurate but also diverse recommendations, since diversification plays an important factor in increasing the users's satisfaction. In this work, we address this problem by proposing a social network hyperbolic embedding that exploits both social connections and user preferences aiming at increasing both the accuracy and the diversity of recommendations.