With the rapid development of social media, personalized recommendation has become an essential means to help people discover attractive and interesting items. Intuitively, users buying items online are influenced not only by their preferences and public attentions, but also by the crowd sentiment (i.e., the word of mouth) to the items. Specifically, users are likely to refuse an item whose most reviews are negative from the crowd. Therefore, a good personalized recommendation model also needs to take crowd sentiment into account, which most current methods do not. In light of this, we propose TSUM, a model that jointly integrates time and crowd sentiment, for personalized recommendation in this paper. TSUM simultaneously models user-oriented topics related to user preferences, time-oriented topics relevant to temporal context, and crowd sentiment towards items. TSUM combines the influences of user preferences, temporal context and crowd sentiment to model user behavior in a unified way. Extensive experimental results on two large real world datasets show that our recommender system significantly outperforms the state-of-the-arts by making more effective personalized recommendations.
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