Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference
文献类型：期刊论文
| 作者 | Fang, Jiansheng; Zhang, Xiaoqing; Hu, Yan; Xu, Yanwu; Yang, Ming; Liu, Jiang |
| 刊名 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
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| 出版日期 | 2021 |
| 关键词 | FACTORIZATION |
| 英文摘要 | Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines. |
| 源URL | [http://ir.nimte.ac.cn/handle/174433/22309]  |
| 专题 | 中国科学院宁波材料技术与工程研究所 2021专题_期刊论文 |
| 作者单位 | 1.Liu, J (corresponding author), Chinese Acad Sci, Cixi Inst Biomed Engn, Ningbo 315201, Peoples R China. 2.Liu, J (corresponding author), Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China. |
| 推荐引用方式 GB/T 7714 | Fang, Jiansheng,Zhang, Xiaoqing,Hu, Yan,et al. Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference[J]. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR),2021. |
| APA | Fang, Jiansheng,Zhang, Xiaoqing,Hu, Yan,Xu, Yanwu,Yang, Ming,&Liu, Jiang.(2021).Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference.2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). |
| MLA | Fang, Jiansheng,et al."Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference".2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) (2021). |
入库方式： OAI收割
来源：宁波材料技术与工程研究所
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