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A group interest-based collaborative filtering algorithm for multimedia information
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DOI：10.1007/s11042-017-5516-x.png)
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Abstract
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This paper proposes a collaborative filtering algorithm based on user group interest. A novel co-clustering method (BalClust) and various weighted non-negative matrix factorization algorithms are used in the proposed method. The BalClust method is used to divide the raw rating matrix into clusters, which are smaller than the original matrix. Then, the balance factor is introduced to consider the user weight and the item-based CF (collaborative filtering). To predict the rating of the unknown items in the cluster, the non-negative matrix factorization algorithm was used. The proposed method achieves higher predicting accuracy and efficiency on low dimensional and homogeneous sub-matrices, and the method also reduces the computational complexity by combining the user and item-based CF. Based on the proposed method, this paper proposed an incremental learning method to ensure data accuracy and timeliness to overcome the problem brought by data updates. The experimental results show the proposed methods outperformed traditional CF algorithms, and the completion time is reduced.
Keywords:
Collaborative filtering
Recommender system
Co-clustering
NMF (non-negative matrix factorization)
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