基于多属性融合的神经协同过滤电影推荐模型
Movie Recommendation Model of Neural Collaborative Filtering Based on Multi Attribute Fusion
DOI: 10.12677/CSA.2023.133030, PDF,   
作者: 冉 伟：天津工业大学计算机科学与技术学院，天津
关键词: 属性；神经协同过滤；注意力机制；Attribute； Neural Collaborative Filtering； Attention Mechanism
摘要: 随着科技和信息技术的发展，“信息过载”使得用户很难在大量电影资源中快速、准确获取自己想要的内容，成为影响用户体验的主要制约因素。因此，个性化的电影推荐成为新时代研究的重点。深度学习的空前发展已经在多个领域取得了很大的成功。为了缓解推荐算法的数据稀疏问题，本文将多种属性信息整合到用户和物品的特征表示中，以获得用户和物品的完整初级特征表示。此外，使用注意机制来区分这些属性的重要性，然后采用拼接和外积两种融合策略建模用户–电影的潜在特征，并分别应用多层感知器和卷积神经网络来充分学习用户与电影之间的非线性交互关系。在电影数据集MovieLens 1M上的实验表明：与传统的DeepCF模型相比，本文提出的模型在命中率和归一化折损累计增益上分别提高了4.19%和0.38%。
Abstract: With the development of science and technology and information technology, “information over-load” makes it difficult for users to quickly and accurately obtain the content they want from a large number of movie resources, which has become a major constraint affecting user experience. There-fore, personalized film recommendation has become the focus of research in the new era. The un-precedented development of deep learning has achieved great success in many fields. In order to alleviate the problem of data sparsity, this paper integrates multiple attribute information into the feature representation of users and items to obtain a complete primary feature representation of users and items. In addition, attention mechanism is used to distinguish the importance of these attributes, and then two fusion strategies, splicing and outer product, are used to model the poten-tial features of user movie, and multi-layer perceptrons and convolutional neural networks are respectively used to fully learn the nonlinear interaction between users and movies. Experiments on the movie dataset MovieLens 1M show that compared with the traditional DeepCF model, the model proposed in this paper improves the hit rate by 4.19% and the normalized cumulative loss gain by 0.38% respectively.
文章引用：冉伟. 基于多属性融合的神经协同过滤电影推荐模型[J]. 计算机科学与应用, 2023, 13(3): 311-318. https://doi.org/10.12677/CSA.2023.133030

参考文献

[1] 项亮. 推荐系统实践[M]. 北京: 人民邮电出版社, 2012.
[2] Ekstrand, M.D., Riedl, J.T. and Konstan, J.A. (2011) Collaborative Filtering Recommender Systems. Foundations and Trends in Human-Computer Interaction, 4, 81-173. [Google Scholar] [CrossRef] 
[3] He, X., Liao, L., Zhang, H., et al. (2017) Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web, Republic and Canton of Geneva, 2017, 173-182. [Google Scholar] [CrossRef] 
[4] Sun, C., Liu, H., Liu, M., et al. (2020) LARA: Attribute-to Feature Adversarial Learning for New-Item Recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, New York, 2020, 582-590. [Google Scholar] [CrossRef] 
[5] Wu, L., Yang, Y., Zhang, K., et al. (2020) Joint Item Recommenda-tion and Attribute Inference: An Adaptive Graph Convolutional Network Approach. Proceedings of the 43rd Interna-tional ACM SIGIR Conference on Research and Development in Information Retrieval, New York, 2020, 679-688. [Google Scholar] [CrossRef] 
[6] Liu, F., Cheng, Z., Zhu, L., et al. (2020) A2-GCN: An Attrib-ute-Aware Attentive GCN Model for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 34, 4077-4088.
[7] Yang, D., Song, Z., Xue, L., et al. (2020) A Knowledge-Enhanced Recommendation Model with At-tribute-Level Co-Attention. Proceedings of the 43rd International ACM SIGIR Conference on Research and Develop-ment in Information Retrieval, New York, 2020, 1909-1912. [Google Scholar] [CrossRef] 
[8] He, X., Du, X., Wang, X., et al. (2018) Outer Product-Based Neural Collaborative Filtering. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, 2018, 2227-2233. [Google Scholar] [CrossRef]