Abstract
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.
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Sun, X., Kong, F., Yang, X., Ye, S. (2005). Using Latent Class Models for Neighbors Selection in Collaborative Filtering. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_18
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DOI: https://doi.org/10.1007/11527503_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
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