基于深度学习的组合推荐算法在电影系统中的应用
Application of Combined Recommendation Algorithm Based on Deep Learning in Movie System
摘要: 如何根据不同用户需求将有效信息第一时间呈现给用户是推荐系统的一个重要研究方向。其中，基于深度学习的推荐系统不仅能解决信息冗余还能提升个性化推荐效果，引起了广泛的关注。由于单一的推荐算法往往并不能理想化地满足客户的需求，本文介绍了一种基于深度学习的组合推荐算法，对基于内容的推荐算法(CB)、矩阵分解推荐算法(MF)和Wide&Deep模型推荐算法进行优化，分别通过岭回归、增加隐式反馈信息和贝叶斯优化的优化方法，并通过转换型的组合方式构成组合推荐算法。实验结果表明，组合算法在Movielens数据集上的推荐效果优于单一模型，评估指标均较于单一模型有基本提升，能够实现精准的内容投放。
Abstract: How to present effective information to users in a timely manner according to their different needs is an important research direction in recommendation systems. Among them, recommendation systems based on deep learning not only solve information redundancy but also improve personalized recommendation performance, which has attracted widespread attention. Due to the fact that a single recommendation algorithm often cannot ideally meet the needs of customers, this article introduces a combination recommendation algorithm based on deep learning, which optimizes content-based recommendation algorithm (CB), matrix factorization recommendation algorithm (MF), and Wide&Deep model recommendation algorithm. The optimization methods include ridge regression, adding implicit feedback information, and Bayesian optimization, and the combination recommendation algorithm is composed of a transformational combination. The experimental results show that the recommendation effect of the combined algorithm on Movielens dataset is better than that of the single model, and the evaluation indicators are basically improved compared with that of the single model, which can achieve accurate content delivery.
文章引用：顾亚鑫, 武国宁. 基于深度学习的组合推荐算法在电影系统中的应用[J]. 计算机科学与应用, 2025, 15(5): 703-717. https://doi.org/10.12677/csa.2025.155142

参考文献

[1] Chen, T., Yin, H., Ye, G., Huang, Z., Wang, Y. and Wang, M. (2020) Try This Instead: Personalized and Interpretable Substitute Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, 25-30 July 2020, 891-900. [Google Scholar] [CrossRef] 
[2] Cheng, H., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., et al. (2016) Wide & Deep Learning for Recommender Systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, 15 September 2016, 7-10. [Google Scholar] [CrossRef] 
[3] Covington, P., Adams, J. and Sargin, E. (2016) Deep Neural Networks for Youtube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, 15-19 September 2016, 191-198. [Google Scholar] [CrossRef] 
[4] He, X., Liao, L., Zhang, H., Nie, L., Hu, X. and Chua, T. (2017) Neural Collaborative Filtering. Proceedings of the 26th International Conference on World Wide Web, Perth, 3-7 April 2017, 173-182. [Google Scholar] [CrossRef] 
[5] He, X., Zhang, H., Kan, M. and Chua, T. (2016) Fast Matrix Factorization for Online Recommendation with Implicit Feedback. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, 17-21 July 2016, 549-558. [Google Scholar] [CrossRef] 
[6] Huang, P., He, X., Gao, J., Deng, L., Acero, A. and Heck, L. (2013) Learning Deep Structured Semantic Models for Web Search Using Clickthrough Data. Proceedings of the 22nd ACM international conference on Information & Knowledge Management, San Francisco, 27 October-1 November 2013, 2333-2338. [Google Scholar] [CrossRef] 
[7] Koplon, R. and Sontag, E.D. (1997) Using Fourier-Neural Recurrent Networks to Fit Sequential Input/output Data. Neurocomputing, 15, 225-248. [Google Scholar] [CrossRef] 
[8] Koren, Y. (2008) Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, 24-27 August 2008, 426-434. [Google Scholar] [CrossRef] 
[9] Liu, H., Wu, F., Wang, W., Wang, X., Jiao, P., Wu, C., et al. (2019) NRPA: Neural Recommendation with Personalized Attention. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, 21-25 July 2019, 1233-1236. [Google Scholar] [CrossRef] 
[10] Pazzani, M.J. and Billsus, D. (n.d.) Content-based Recommendation Systems. In: Brusilovsky, P., Kobsa, A. and Nejdl, W., Eds., The Adaptive Web, Springer, 325-341. [Google Scholar] [CrossRef] 
[11] Sarwar, B.M. (2001) Sparsity, Scalability, and Distribution in Recommender Systems. University of Minnesota.
[12] Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2001) Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 1-5 May 2001, 285-295. [Google Scholar] [CrossRef] 
[13] Wang, C. and Blei, D.M. (2011) Collaborative Topic Modeling for Recommending Scientific Articles. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, 21-24 August 2011, 448-456. [Google Scholar] [CrossRef] 
[14] Wang, H., Wang, N. and Yeung, D. (2015) Collaborative Deep Learning for Recommender Systems. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, 10-13 August 2015, 1235-1244. [Google Scholar] [CrossRef] 
[15] Wu, C., Ahmed, A., Beutel, A., Smola, A.J. and Jing, H. (2017) Recurrent Recommender Networks. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, 6-10 February 2017, 495-503. [Google Scholar] [CrossRef] 
[16] Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X. and Zhou, X. (2015) Joint Modeling of Users’ Interests and Mobility Patterns for Point-of-Interest Recommendation. Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, 26-30 October 2015, 819-822. [Google Scholar] [CrossRef] 
[17] Zhang, F., Yuan, N.J., Lian, D., Xie, X. and Ma, W. (2016) Collaborative Knowledge Base Embedding for Recommender Systems. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 353-362. [Google Scholar] [CrossRef] 
[18] Zhang, H., Shen, F., Liu, W., He, X., Luan, H. and Chua, T. (2016) Discrete Collaborative Filtering. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, 17-21 July 2016, 325-334. [Google Scholar] [CrossRef] 
[19] Zhang, J., Lin, Y., Song, Z., et al. (2018) Learning Long Term Dependencies via Fourier Recurrent Units. arXiv: 1803.06585. [Google Scholar] [CrossRef] 
[20] Zhang, S., Yao, L., Sun, A., et al. (2019) Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys (CSUR), 52, 1-38. [Google Scholar] [CrossRef] 
[21] Zheng, L., Noroozi, V. and Yu, P.S. (2017) Joint Deep Modeling of Users and Items Using Reviews for Recommendation. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, 6-10 February 2017, 425-434. [Google Scholar] [CrossRef] 
[22] Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., et al. (2018) Deep Interest Network for Click-Through Rate Prediction. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 1059-1068. [Google Scholar] [CrossRef]