Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/134899
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Type: | Conference paper |
Title: | Variational deep collaborative matrix factorization for social recommendation |
Author: | Xiao, T. Tian, H. Shen, H. |
Citation: | Lecture Notes in Artificial Intelligence, 2019 / Yang, Q., Zhou, Z.H., Gong, Z., Zhang, M.L., Huang, S.J. (ed./s), vol.11439, pp.426-437 |
Publisher: | Springer |
Publisher Place: | Switzerland |
Issue Date: | 2019 |
Series/Report no.: | Lecture Notes in Artificial Intelligence |
ISBN: | 9783030161477 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (14 Apr 2019 - 17 Apr 2019 : Macau, China) |
Editor: | Yang, Q. Zhou, Z.H. Gong, Z. Zhang, M.L. Huang, S.J. |
Statement of Responsibility: | Teng Xiao, Hui Tian, and Hong Shen |
Abstract: | 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. |
Keywords: | Recommender System; Matrix Factorization; Deep Learning; Generative mode |
Rights: | © Springer Nature Switzerland AG 2019 |
DOI: | 10.1007/978-3-030-16148-4_33 |
Grant ID: | http://purl.org/au-research/grants/arc/DP150104871 |
Published version: | https://link.springer.com/ |
Appears in Collections: | Computer Science publications |
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