GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Extended latent class models for collaborative recommendation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Collaborative filtering is becoming a popular technique for reducing information overload. However, most of current collaborative filtering algorithms have three major limitations: accuracy, data sparsity and scalability. In this paper, we propose a new collaborative filtering algorithm to solve the problem of data sparsity and improve the prediction accuracy. If the rated items amount of a user is less than some threshold, the algorithm utilizes the output of latent class models for neighbors selection, then uses the neighborhood-based method to produce the prediction of unrated items, otherwise it predicts the rating using the STIN1 method. Our experimental results show that our algorithm outperforms the conventional neighborhood-based method and the STIN1 method.