Towards Diversity in Recommendations Using Social Networks

While there has been a lot of research towards improving the accuracy of recommender systems, the resulting systems have tended to become increasingly narrow in suggestion variety. An emerging trend in recommendation systems is to actively seek out diversity in recommendations, where the aim is to provide unexpected, varied, and serendipitous recommendations to the user. Our main contribution in this paper is a new approach to diversity in recommendations called "Social Diversity," a technique that uses social network information to diversify recommendation results. Social Diversity utilizes social networks in recommender systems to leverage the diverse underlying preferences of different user communities to introduce diversity into recommendations. This form of diversification ensures that users in different social networks (who may not collaborate in real life, since they are in a different network) share information, helping to prevent siloization of knowledge and recommendations. We describe our approach and show its feasibility in providing diverse recommendations for the MovieLens dataset