Personalized Resource Recommendation Based on Regular Tag and User Operation

In conventional tag-based recommendation system, the sparsity and impurity of social tag data significantly increase the complexity of data processing and affect the accuracy of recommendation. To address these problems, we consider from the perspective of resource provider and propose a resource recommendation framework based on regular tags and user operation feedbacks. Based on these concepts, we design the user feature representation integrating the information of regular tags, user operations and time factor, so as to precisely discover the user preference on different tags. The personalized recommendation algorithm is designed based on collaborative filtering mechanism by analyzing the general preference modeling of different users. We conduct the experimental evaluation on a real recommendation system with extensive user and tag data. Compared with traditional user-based collaborative filtering and the social-tag-based collaborative filtering, our approach can effectively alleviate the sparsity problem of tag data and user rating data, and our proposed user feature is more accurate to improve the performance of the recommendation system.