一种压缩稀疏用户评分矩阵的协同过滤算法

Abstract

Abstract:

The paper proposes a novel memory-based collaborative filtering algorithm—Multi-label Probabilistic Latent Semantic Analysis based Collaborative Filtering, which improves the quality of recommendations by reducing the dimension of the user-rating-data matrix by multi-label probabilistic latent semantic analysis when the matrix is extremely sparse. Firstly, it confines the set of latent variables of probability latent semantic analysis to the set of multi-label of items to make latent variables have meanings of corresponding labels. Then it learns the probabilistic distribution of latent variables, i. e., the model of use's interest, to compress the user-rating-data matrix. Finally, it computes the similarity between different users based on the above learned model and makes recommendations. Compared to memory-based collaborative filtering algorithms, the proposed algorithm decreases the mean absolute error 4 percents averagely on test dataset by reducing the dimension of the user-rating-data matrix. The proposed algorithm makes the recommendation system understandable and obtains competitive recommendations compared to the filtering algorithm which reduces the dimension of the user-rating-data matrix by probabilistic latent semantic analysis.

Key words: multi-label of items, probabilistic latent semantic analysis, iterative method, collaborative filtering, algorithms

CLC Number:

TP181

HOU Cui-qin;JIAO Li-cheng;ZHANG Wen-ge. Collaborative filtering algorithm via compressing the sparse user-rating-data matrix[J].J4, 2009, 36(4): 614-638.

References

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Collaborative filtering algorithm via compressing the sparse user-rating-data matrix

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