A Social Tagging Based Collaborative Filtering Recommendation Algorithm for Digital Library

Recommendation is one of the new personalized services in the digital library. This paper proposes a new collaborative filtering recommendation algorithm based on the social tagging, which try to settle the semantic gap and the cold start problems of traditional collaborative filtering. Firstly, the communities with the similar habits are detected in the social network of the digital library. Then the candidate tags are derived from the user-book-tag correlation model. Finally, the books with highest posterior of the tags are recommended by the naive Bayes classifier. Experiments results show that the proposed algorithm improves the performance of the collaborative filtering algorithms. And it has been a core recommendation algorithm in China Academic Digital Associative Library (CADAL).