Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ReCoM: reinforcement clustering of multi-type interrelated data objects
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A study of methods for normalizing user ratings in collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
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The aim of collaborative filtering is to make predictions for active user by utilizing the rating information of likeminded users in a historical database. But previous methods suffered from problems: sparsity, scalability, rating bias etc. To alleviate those problems, this paper presents a novel approach—Iterative Rating Filling Collaborative Filtering algorithm (IRFCF). Firstly, based on the idea of iterative reinforcement process, object-pair similarity is computed iteratively, and average rating and rating range are introduced to normalize ratings in order to alleviate rating bias problem. Then missing ratings are filled from user and item clusters through iterative clustering process to solve the sparsity and scalability problems. Finally, the nearest neighbors in the set of top clusters are selected to generate predictions for active user. Experimental results have shown that our proposed collaborative filtering approach can provide better performance than other collaborative filtering algorithms.