Variational deep collaborative matrix factorization for social recommendation

Teng Xiao, Hui Tian, Hong Shen

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose a Variational Deep Collaborative Matrix Factorization (VDCMF) algorithm for social recommendation that infers latent factors more effectively than existing methods by incorporating users’ social trust information and items’ content information into a unified generative framework. Unlike neural network-based algorithms, our model is not only effective in capturing the non-linearity among correlated variables but also powerful in predicting missing values under the robust collaborative inference. Specifically, we use variational auto-encoder to extract the latent representations of content and then incorporate them into traditional social trust factorization. We propose an efficient expectation-maximization inference algorithm to learn the model’s parameters and approximate the posteriors of latent factors. Experiments on two sparse datasets show that our VDCMF significantly outperforms major state-of-the-art CF methods for recommendation accuracy on common metrics.

原文English
主出版物標題Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings
編輯Qiang Yang, Zhiguo Gong, Zhi-Hua Zhou, Sheng-Jun Huang, Min-Ling Zhang
發行者Springer Verlag
頁面426-437
頁數12
ISBN(列印)9783030161477
DOIs
出版狀態Published - 2019
對外發佈
事件23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 - Macau, China
持續時間: 14 4月 201917 4月 2019

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11439 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

Conference23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019
國家/地區China
城市Macau
期間14/04/1917/04/19

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