Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
User Profile Modeling and Applications to Digital Libraries
ECDL '99 Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Social Tagging in Digital Archives
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Survey on social tagging techniques
ACM SIGKDD Explorations Newsletter
A collaborative filtering based re-ranking strategy for search in digital libraries
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
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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 naïve 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).