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- Personalized recommender systems are confronting great challenges of accuracy (1)
- and are therefore potential to help in providing better recommendations. In this article (1)
- diversification and novelty (1)
- diversification and novelty of recommendations. (1)
- especially when the data set is sparse and lacks accessorial information (1)
- item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents (1)
- such as user profiles (1)
- to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy (1)
- we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets (1) More Less