Domain-specific user preference prediction based on multiple user activities

Yunfei Long, Qin Lu, Yue Xiao, Minglei Li, Chu-Ren Huang

Abstract: Inferring latent user preferences using both structured and unstructured data is an important social computing task. In this paper, we propose a user preference representation based on user activities embedded in unstructured data to better encode the homophily theory. The representation of an individual user is learned using a embedding based method to integrate latent user preferences in social media. The method has the ability to integrate a variety of user activities based cues from user comments, user social network (i.e; follower/followee connections) and user interested topics which are indicated by the topics a user has participated in. Experiments are conducted to evaluate the prediction of each user's favorite team as a part of user preferences in a dataset collected from the Hu-pu basketball discussion forum.1 Results clearly indicate that our proposed user representation outperforms other user representation baselines. Integrating user social network and user interested topics with user comments can improve the overall performance of user preference prediction.