Volume 22, Issue 1 (JIAEEE Vol.22 No.1 2025)
Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(1): 121-131 |
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Alinejad S, Koohestani B, Feizi Derakhshi M R. Optimal modeling of interactions between users and items in recommender systems using an improved deep reinforcement learning method. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (1) :121-131
URL: http://jiaeee.com/article-1-1628-en.html
URL: http://jiaeee.com/article-1-1628-en.html
University of Tabriz
Abstract: (539 Views)
Recommender systems are one of the most important topics in academia and industry. With the increase in the volume of information and data, it has become confusing and sometimes impossible for users to access the required services without using recommender systems. So far, various techniques have been proposed for this purpose such as collaborative filtering, matrix factorization, logistic regression, neural networks, etc. However, most of these methods suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems; (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. In this research, the modeling of interactions between users and items is done using an improved deep reinforcement learning method which can consider both the dynamic adaptation and long term rewards. The results of the experiments show that the proposed algorithm performs better than other methods.
Keywords: Recommender systems, Deep reinforcement learning, Artificial intelligence, User item interactions
Type of Article: Research |
Subject:
Electronic
Received: 2023/08/20 | Accepted: 2024/08/1 | Published: 2025/05/29
Received: 2023/08/20 | Accepted: 2024/08/1 | Published: 2025/05/29
Extended Abstract [PDF 244 KB] (9 Download)
References
1. [1] R. J. Kuo and S.-S. Li, "Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review", Applied Soft Computing, vol. 135, p. 110038, 2023/03/01/ 2023. [DOI:10.1016/j.asoc.2023.110038]
2. [2] Y. Koren, S. Rendle, and R. Bell, "Advances in collaborative filtering", Recommender systems handbook, pp. 91-142, 2021. [DOI:10.1007/978-1-0716-2197-4_3]
3. [3] R. J. Mooney and L. Roy, "Content-based book recommending using learning for text categorization", presented at the Proceedings of the fifth ACM conference on Digital libraries, San Antonio, Texas, USA, 2000. [DOI:10.1145/336597.336662]
4. [4] D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, "A content-based recommender system for computer science publications", Knowledge-Based Systems, vol. 157, pp. 1-9, 2018/10/01/ 2018. [DOI:10.1016/j.knosys.2018.05.001]
5. [5] S. Rendle, W. Krichene, L. Zhang, and J. Anderson, "Neural collaborative filtering vs. matrix factorization revisited", in Proceedings of the 14th ACM Conference on Recommender Systems, 2020, pp. 240-248. [DOI:10.1145/3383313.3412488]
6. [6] Y. Koren, R. M. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems", IEEE Computer, vol. 42, no. 8, pp. 30-37, 2009. [DOI:10.1109/MC.2009.263]
7. [7] S. Rendle, W. Krichene, L. Zhang, and J. Anderson, "Neural collaborative filtering vs. matrix factorization revisited", in Proceedings of the 14th ACM Conference on Recommender Systems, 2020, pp. 240-248. [DOI:10.1145/3383313.3412488]
8. [8] A. Mnih and R. R. Salakhutdinov, "Probabilistic matrix factorization", Advances in neural information processing systems, vol. 20, 2007.
9. [9] Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model", in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 426-434. [DOI:10.1145/1401890.1401944]
10. [10] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning", Nature, vol. 521, no. 7553, pp. 436-444, 2015/05/01 2015, doi: 10.1038/nature14539. [DOI:10.1038/nature14539]
11. [11] W. Zhang, T. Du, and J. Wang, "Deep learning over multi-field categorical data - - A case study on user response prediction", in ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings, 2016, pp. 45-57. [DOI:10.1007/978-3-319-30671-1_4]
12. [12] H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah, "Wide & deep learning for recommender systems", CoRR, vol. abs/1606.07792, 2016. [DOI:10.1145/2988450.2988454]
13. [13] K. Falk, Practical recommender systems. Simon and Schuster, 2019.
14. [14] V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, "An Introduction to Deep Reinforcement Learning", Foundations and Trends® in Machine Learning, vol. 11, no. 3-4, pp. 219-354, 2018, doi: 10.1561/2200000071. [DOI:10.1561/2200000071]
15. [15] T. Hickling, A. Zenati, N. Aouf, and P. Spencer, "Explainability in deep reinforcement learning: A review into current methods and applications", ACM Comput. Surv., vol. 56, no. 5, dec 2023. [DOI:10.1145/3623377]
16. [16] ملائی، مهدی. امیرخانی، عبدالله. "رانندگی خودکار در محیط بزرگراه مبتنی بر یادگیری سیاست با استفاده از روش‌های یادگیری تقویتی توزیعی"، نشریه مهندسی برق و الکترونیک ایران، دوره 19، شماره 2، 1401.
17. [17] بیگی، اکرم. اکبریان، امین. "افزایش سودآوری بازار شبکه های هوشمند برق با تکنیک یادگیری تقویتی عملگر ـ نقاد"، نشریه مهندسی برق و الکترونیک ایران، دوره 19، شماره 1، 1401.
18. [18] X. Chen, L. Yao, J. McAuley, G. Zhou, and X. Wang, "Deep reinforcement learning in recommender systems: A survey and new perspectives", Knowledge-Based Systems, vol. 264, p. 110335, 2023. [DOI:10.1016/j.knosys.2023.110335]
19. [19] Y. Zhang, C. Zhang, and X. Liu, "Dynamic scholarly collaborator recommendation via competitive multi-agent reinforcement learning", in Proceedings of the Eleventh ACM Conference on Recommender Systems, ser. RecSys '17. New York, NY, USA: Association for Computing Machinery, 2017, p. 331-335. [DOI:10.1145/3109859.3109914]
20. [20] X. Chen, S. Li, H. Li, S. Jiang, Y. Qi, and L. Song, "Generative adversarial user model for reinforcement learning based recommendation system", in International Conference on Machine Learning. PMLR, 2019, pp. 1052-1061.
21. [21] X. Bai, J. Guan, and H. Wang, "A model-based reinforcement learning with adversarial training for online recommendation", Advances in Neural Information Processing Systems, vol. 32, 2019.
22. [22] D. Hong, Y. Li, and Q. Dong, "Nonintrusive-sensing and reinforcementlearning based adaptive personalized music recommendation", in Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, 2020, pp. 1721-1724. [DOI:10.1145/3397271.3401225]
23. [23] X. Zhao, L. Xia, L. Zou, H. Liu, D. Yin, and J. Tang, "Wholechain recommendations", in Proceedings of the 29th ACM international conference on information & knowledge management, 2020, pp. 1883-1891. [DOI:10.1145/3340531.3412044]
24. [24] A. Montazeralghaem and J. Allan, "Extracting relevant information from user's utterances in conversational search and recommendation", in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 1275-1283. [DOI:10.1145/3534678.3539471]
25. [25] G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, "Drn: A deep reinforcement learning framework for news recommendation", in Proceedings of the 2018 world wide web conference, 2018, pp. 167-176. [DOI:10.1145/3178876.3185994]
26. [26] X. Zhao, C. Gu, H. Zhang, X. Yang, X. Liu, J. Tang, and H. Liu, "Dear: Deep reinforcement learning for online advertising impression in recommender systems", in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 1, 2021, pp. 750-758. [DOI:10.1609/aaai.v35i1.16156]
27. [27] F. Pan, Q. Cai, P. Tang, F. Zhuang, and Q. He, "Policy gradients for contextual recommendations", in The World Wide Web Conference, 2019, pp. 1421-1431. [DOI:10.1145/3308558.3313616]
28. [28] F. Liu, R. Tang, X. Li, W. Zhang, Y. Ye, H. Chen, H. Guo, and Y. Zhang, "Deep reinforcement learning based recommendation with explicit useritem interactions modeling", arXiv preprint arXiv:1810.12027, 2018.
29. [29] Q. Cai, A. Filos-Ratsikas, P. Tang, and Y. Zhang, "Reinforcement mechanism design for e-commerce", in Proceedings of the 2018 World Wide Web Conference, 2018, pp. 1339-1348. [DOI:10.1145/3178876.3186039]
30. [30] K. Zhao, X. Wang, Y. Zhang, L. Zhao, Z. Liu, C. Xing, and X. Xie, "Leveraging demonstrations for reinforcement recommendation reasoning over knowledge graphs", in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 2020, pp. 239-248. [DOI:10.1145/3397271.3401171]
31. [31] M. van Otterlo and M. Wiering, "Reinforcement Learning and Markov Decision Processes", in Reinforcement Learning: State-of-the-Art, M. Wiering and M. van Otterlo Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 3-42. [DOI:10.1007/978-3-642-27645-3_1]
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