SCMF: Sparse covariance matrix factorization for collaborative filtering
Refereed conference paper presented and published in conference proceedings


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摘要Matrix factorization (MF) is a popular collaborative filtering approach for recommender systems due to its simplicity and effectiveness. Existing MF methods either assume that all latent features are uncorrelated or assume that all are correlated. To address the important issue of what structure should be imposed on the features, we investigate the covariance matrix of the latent features learned from real data. Based on the findings, we propose an MF model with a sparse covariance prior which favors a sparse yet non-diagonal covariance matrix. Not only can this reflect the semantics more faithfully, but imposing sparsity can also have a side effect of preventing over fitting. Starting from a probabilistic generative model with a sparse covariance prior, we formulate the model inference problem as a maximum a posteriori (MAP) estimation problem. The optimization procedure makes use of stochastic gradient descent and majorization minimization. For empirical validation, we conduct experiments using the MovieLens and Netflix datasets to compare the proposed method with two strong baselines which use different priors. Experimental results show that our sparse covariance prior can lead to performance improvement.
著者Shi J., Wang N., Xia Y., Yeung D.-Y., King I., Jia J.
會議名稱23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
會議開始日03.08.2013
會議完結日09.08.2013
會議地點Beijing
會議國家/地區中國
出版年份2013
月份12
日期1
頁次2705 - 2711
國際標準書號9781577356332
國際標準期刊號1045-0823
語言英式英語

上次更新時間 2020-05-09 於 02:00