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.