Leveraging community imparted data for personalized recommendation is one of the active research problems since there are rich contexts and human activities in such explosively growing data. We focus on personalized travel recommendation and show promising applications. We conduct personalized travel recommendation by considering specific user profiles or attributes. We propose a personalized t...

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 ...

Recommender systems are being used to help users find relevant items from a large set of alternatives in many online applications. Most existing recommendation techniques have focused on improving recommendation accuracy; however, diversity of recommendations has also been increasingly recognized in research literature as an important aspect of recommendation quality. This paper proposes a grap...