Provide a Personalized Session-Based Recommender System with Self-Attention Networks
Subject Areas : electrical and computer engineering
Azam
Ramazani
1
(Yazd University)
A.
Zareh
2
Keywords: Personalized recommendation, session-based recommendation, self-attention network, deep learning,
Abstract :
Session-based recommender systems predict the next behavior or interest of the user based on user behavior and interactions in a session, and suggest appropriate items to the user accordingly. Recent studies to make recommendations have focused mainly on the information of the current session and ignore the information of the user's previous sessions. In this paper, a personalized session-based recommender model with self-attention networks is proposed, which uses the user's previous recent sessions in addition to the current session. The proposed model uses self-attention networks (SANs) to learn the global dependencies among all session items. First, SAN is trained based on anonymous sessions. Then for each user, the sequences of the current session and previous sessions are given to the network separately, and by weighted combining the ranking results from each session, the final recommended items are obtained. The proposed model is tested and evaluated on real-world Reddit dataset in two criteria of accuracy and mean reciprocal rank. Comparing the results of the proposed model with previous approaches indicates the ability and effectiveness of the proposed model in providing more accurate recommendations.
[1] R. Salakhutdinov, A. Mnih, and G. Hinton, "Restricted Boltzmann machines for collaborative filtering," in Proc. of the 24th Int. Conf. on Machine Learning, ICML'07, pp. 791-798, Corvallis, OR, USA, 20-24 Jun. 2007.
[2] S. Wang, L. Cao, Y. Wang, Q. Z. Sheng, M. A. Orgun, and D. Lian, "A survey on session-based recommender systems," ACM Computing Surveys, vol. 54, no. 7, pp. 1-38, May 2021.
[3] G. Shani, D. Heckerman, R. I. Brafman, and C. Boutilier, "An MDP-based recommender system," J. of Machine Learning Research, vol. 6, no. 9, pp. 1265-1295, 2005.
[4] S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, "Factorizing personalized markov chains for next-basket recommendation," in Proc. of the 19th Int.Conf. on World Wide Web, WWW '10, pp. 811-820, Raleigh, NC, USA, 26-30 Apr. 2010.
[5] B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, "Session-based recommendations with recurrent neural networks," in Proc. of the 4th Int. Conf. on Learning Representations, ICLR'16, 10 pp., San Juan, Puerto Rico, 2-4 May 2016.
[6] Y. K. Tan, X. Xu, and Y. Liu, "Improved recurrent neural networks for session-based recommendations," in Proc. of the 1st Workshop on Deep Learning for Recommender Systems, DLRS’16, pp. 17-22, Boston, MA, USA, 15-15 Sept. 2016.
[7] D. Jannach and M. Ludewig, "When recurrent neural networks meet the neighborhood for session-based recommendation," in Proc. of the 11th ACM Conf. on Recommender Systems, RecSys'17, pp. 306-310, Como, Italy, 27-31 Aug. 2017.
[8] J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, "Neural attentive session-based recommendation," in Proc. of the ACM Conf. on Information and Knowledge Management, CIKM’17, pp. 1419-1428, Singapore, 6-10 Nov. 2017.
[9] M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, "Personalizing session-based recommendations with hierarchical recurrent neural networks," in Proc. of the 11th ACM Conf. on Recommender Systems, RecSys'17, pp. 130-137, Como, Italy, 27-31 Aug. 2017.
[10] Z. Zhang and B. Wang, "Learning sequential and general interests via a joint neural model for session-based recommendation," Neurocomputing, vol. 415, pp. 165-173, 20 Nov. 2020.
[11] S. Wu, et al., "Session-based recommendation with graph neural networks," in Proc. of the AAAI Conf. on Artificial Intelligence, pp. 346-353, Honolulu, HI, USA, 27 Jan.-1 Feb. 2019.
[12] F. Yu, Y. Zhu, Q. Liu, S. Wu, L. Wang, and T. Tan, "TAGNN: target attentive graph neural networks for session-based recommendation," in Proc. of the 43rd Int ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 1921-1924, Xian, China, 25- 30 Jul. 2020.
[13] T. Chen and R. C. W. Wong, "Handling information loss of graph neural networks for session-based recommendation," in Proc. of the 26th ACM SIGKDD Inte. Conf. on Knowledge Discovery & Data Mining, pp. 1172-1180, San Diego, California, 6-10 Jul. 2020.
[14] A. Vaswani, et al., "Attention is all you need," in Proc. of the 31st Conf. on Neural Information Processing Systems, NIPS’17, pp. 5998-6008, Long Beach, CA, USA, 4-9 Dec. 2017.
[15] J. Fang, Session-Based Recommendation with Self-Attention Networks, arXiv preprint arXiv:2102.01922, Feb. 2021.
[16] P. H. Anh, N. X. Bach, and T. M. Phuong, "Session-based recommendation with self-attention," in Proc. of the 10th Int. Symp. on Information and Communication Technology, SoICT'19, 8 pp., Ha Long Bay, Vietnam, 4-6 Dec. 2019.
[17] R. Mehta and K. Rana, "A review on matrix factorization techniques in recommender systems," in Proc. of the 2nd Int. Conf. on Communication Systems, Computing and IT Applications, CSCITA’17, pp. 269-274, Mumbai, India, 7-8 Apr. 2017.
[18] R. Salakhutdinov and A. Mnih, "Probabilistic matrix factorization," in Proc. of the 20th Int. Conf. on Neural Information Processing Systems, NIPS'07, pp. 1257-1264,Vancouver,Canada, 3-6 Dec. 2007.
[19] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proc. of the 10th Int. Conf. on World Wide Web, WWW'01, pp. 285-295, Hong Kong, China, 1-5 May 2001.
[20] R. He and J. McAuley, "Fusing similarity models with markov chains for sparse sequential recommendation," in Proc. of the 16th Int. Conf. on Data Mining, ICDM'16, pp. 191-200, Barcelona, Spain, 12-15 Dec. 2016.
[21] S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. Orgun, "Sequential recommender systems: challenges, progress and prospects," in Proc. of the Int. Joint Conf. on Artificial Intelligence, IJCAI'19, pp. 6332-6338, Macao, China,10-16 Aug. 2019.
[22] Q. Liu, Y. Zeng, R. Mokhosi, and H. Zhang, "STAMP: short-term attention/memory priority model for session-based recommendation," in Proc. of the 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, pp. 1831-1839, London, United Kingdom, 19-23 Aug. 2018.
[23] M. Wang, et al., "A collaborative session-based recommendation approach with parallel memory modules," in Proc. of the 42nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 345-354, Paris, France, 21-25 Jul. 2019.
[24] C. Xu, et al., "Graph contextualized self-attention network for session-based recommendation," in Proc. of the 28th Int. Joint Conf. on Artificial Intelligence, IJCAI’19, pp. 3940-3946, Macao, China, 10-16 Aug. 2019.
[25] J. Wang, Q. Xu, J. Lei, C. Lin, and B. Xiao, "PA-GGAN: session-based recommendation with position-aware gated graph attention network," in Proc. of the IEEE Int. Conf. on Multimedia and Expo, ICME’20, 6 pp., London, UK, 6-10 Jul. 2020.
[26] M. Ruocco, O. S. L. Skrede, and H. Langseth, "Inter-session modeling for session-based recommendation," in Proc. of the 2nd Workshop on Deep Learning for Recommender Systems, DLRS17, pp. 24-31, Como, Italy, 27-27 Aug. 2017.
[27] J. You, Y. Wang, A. Pal, P. Eksombatchai, C. Rosenburg, and J. Leskovec, "Hierarchical temporal convolutional networks for dynamic recommender systems," in Proc. of the World Wide Web Conf., WWW'19, pp. 2236-2246, San Francisco, CA, USA, 13-17 May 2019.
[28] M. Ludewig and D. Jannach, "Evaluation of session-based recommendation algorithms," User Modeling and User-Adapted Interaction, vol. 28, no. 4-5, pp. 331-390, Dec. 2018.