Recommendation for online social feeds by exploiting user response behavior

Ping-Han Soh, Yu-Chieh Lin, Ming-Syan Chen

Abstract: In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second. The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if users access these networks via mobile devices. To assist users in discovering interesting messages efficiently, in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user's response behavior. We extract data from the most popular social network, and the experimental results show that the proposed approach is effective and efficient.