Modeling Evolutionary Behaviors for Community-based Dynamic Recommendation

We exploit dynamic patterns from both documents' and users' aspects to build models for recommendation. We propose a Community-Based Dynamic Recommendation (CBDR) scheme to make recommendations by taking content semantics, evolutionary patterns, and user communities into consideration. A Time-Sensitive Adaboost algorithm is proposed to build adaptive user models for ranking document candidates based on leveraging dynamic factors such as freshness, popularity, and other attributes. Our experimental results on a large online application system demonstrate the recommendation usefulness of the CBDR scheme is 259% better than the collaborative filtering, 126% better than the community-based static recommendation algorithm, and 106% better than the optimal global recommendation bound.