Personalized recommender systems based on social relationships and historical behaviors

Recommender systems have a wide range of applications in the age suffering information overload. A promising way to design better recommender systems in the presence of ubiquitous social media is to utilize social relationships in recommendation algorithms, named social recommendation. One critical challenge in social recommendation is how to mine valuable information intrinsic to social relationships and integrate such information into the algorithm design. In this paper, we argue that both social relationships and historical behaviors are affected by the same implicit factors. For example, due to the existence of implicit factors such as peer influence or common interests in social networks, users with similar implicit factors will have a high probability to become friends and collect similar objects. Accordingly, we propose a recommendation algorithm that jointly utilizes social relationships and historical behaviors, based on the extended linear optimization technique. We test the performance of our algorithm for four groups of users on real networks, including all users, active users, inactive users and cold-start users. Results show that, in all the above four scenarios, the proposed algorithm performs overall best subject to accuracy and diversity metrics compared with the benchmarks. In particular, the algorithm remarkably improves the recommendation performance for cold-start users. Further analysis shows that the contribution of social relationships depends on the coupling strength between social relationships and historical behaviors.