Session-Based Recommendation -- Case Study on Tencent Weibo

Ten cent Weibo is one of the largest micro-blogging websites in China. There are more than 200 million registered users on Ten cent Weibo, generating over 40 million messages each day. Recommending appealing items to users is a mechanism to reduce the risk of information overload. The task of this paper is to predict whether or not a user will follow an item that has been recommended to the user by Ten cent Weibo. This paper contains two parts: predicting users' interests and distinguish whether the user is busy or available to browse recommended items. We apply several model based collaborative filtering as well as content-based filtering to capture users' interests. Besides, we built an occupied model to distinguish users' state and combined with recommendations methods as the final result. In the paper, we used session-based hamming loss as performance measure. The hamming loss was greatly reduced (40%) with occupied model from 0.187 to 0.13.