Proposing a new friend recommendation method, FRUTAI, to enhance social media providers' performance

摘要

Social media, such as Facebook and Twitter, have grown rapidly in recent years. Friend recommendation systems, as an important emerging component of social media, may efficiently expand social media networks by proactively recommending new and potentially high-quality friends to users. Literature review has shown that prior research work on friend recommendation mainly focuses on the linking relation between users in social media but largely neglects the influence of users' attributes. In this study, we have systematically reviewed and evaluated the existing state-of-the-art friend recommendation algorithms. We introduce a new Friend Recommendation system using a User's Total Attributes Information (FRUTAI) based on the law of total probability. The proposed method can be easily extended according to the increasing number of a user's attributes with low computation cost. Furthermore, the FRUTAI is a universal friend recommendation method and can be applied in different types of social media because it does not distinguish the structure of the network. We have collected 7 million users' public information and their friend relationships from RenRen, commonly regarded as the Facebook of China. Using the real-world data from a dominant social media provider, we extensively evaluate the proposed method with other existing friend recommendation algorithms. Our experimental results have demonstrated the comparatively better performance of FRUTAI. In our empirical studies, we have observed that the performance of FRUTAI is related to the number of a user's friends. In particular, when a user has a small number of friends, the proposed FRUTAI algorithm performs better than other algorithms; when a user has a large number of friends, the overall performance of FRUTAI becomes less competitive but is still comparable to those of other providers, and its precision rate is quite outstanding. Our findings may provide some important practical implications to social media design and performance.