Session-Based Commodity Recommendation Through Interactive Attention and Parameter Self-Adaption
ZHENG Nan1, GUO Yi1,2,3, LI Zhiqiang1, WANG Zhihong1
1.Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; 2.Business Intelligence and Visualization Research Center, National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436, China; 3.Shanghai Engineering Research Center of Big Data & Internet Audience, Shanghai 200072, China
Abstract：In the E-Commence scenario, it is a crucial issue to construct an effective Session-Based Recommendation (SBR) model, helping users quickly find products of interest based on their short-term interests. Most current models fails to capture the interactive relationship of item embeddings on the session graph and global graph. This paper proposes to utilize the interactive attention and improved parameter self-adaptive strategy to enhance the GNN-based commodity recommendation model. The interactive attention layer is applied to amend the commodity representation in the global graph and the session graph through strong correlation extraction, and the parameter adaptive layer is to obtain the final representation of the items for prediction. Experimental results show that our model is significantly superior to other off-the-shelf models on the public dataset of Tmall.
[1] JIANG L, CHENG Y, YANG L, et al. A trust-based collaborative filtering algorithm for e-commerce recommendation system[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(8):3023-3034. [2] GUO Y, LING Y, CHEN H, et al. A time-aware graph neural network for session-based recommendation[J]. IEEE Access, 2020, 8:16737-167382. [3] WU S, TANG Y, ZHU Y, et al. Session-based recommendation with graph neural networks[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019:346-353. [4] WANG Z, WEI W, CONG G, et al. Global context enhanced graph neural networks for session-based recommendation[C]//Proceedings of the 43rd International conference on Research and Development in Information Retrieval, 2020:169-178. [5] 曾安,聂文俊.基于深度双向LSTM的股票推荐系统[J].计算机科学,2019,46(10):84-89. [6] XIA H, LI J J, LIU Y, et al. Collaborative filtering recommendation algorithm based on attention GRU and adversarial learning[J]. IEEE Access,2020,8: 208149-208157. [7] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[C]//Proceedings of the 4th International Conference on Learning Representations, 2016: 1-9. [8] BOGINA V, KUFLIK T. Incorporating dwell time in session-based recommendations with recurrent neural networks[C]//Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems, 2017: 57-59. [9] WANG C, MCAULEY J. Self-Attentive sequential recommendation[C]//Proceedings of International Conference on Data Mining, 2018:197-206. [10] 陈海涵,吴国栋,李景霞,等.基于注意力机制的深度学习推荐研究进展[J].计算机工程与科学,2021,43(02):370-380. [11] ZHOU G, ZHU X, SONG C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th International Conference on Knowledge Discovery and Data Mining, 2018:1059-1068. [12] ZHOU G, MOU N, FAN Y, et al. Deep interest evolution network for click-through rate prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 5941-5948. [13] FENG Y, LV F,SHEN W, et al. Deep session interest network for click-through rate prediction[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019:2301-2307. [14] 南宁,杨程屹,武志昊.基于多图神经网络的会话感知推荐模型[J].计算机应用,2021,41(02):330-336. [15] 王英博,孙永荻.基于GNN的矩阵分解推荐算法[J/OL]. http://kns.cnki.net/kcms/detail/11.2127.TP.20201231.1301.012.html. [2021-03-08]. [16] WANG W, ZHANG W, LIU S, et al. Beyond clicks: modeling multi-relational item graph for session-based target behavior prediction[C]//Proceedings of the Web Conference, 2020: 3056-3062. [17] 李智强, 过弋, 王志宏. 多类型注意力下参数自适应的多标签文本分类[J].中文信息学报, 2022,36(10): 116-125. [18] SARWAR B, KARYPIS G, KONSTAN, J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web, 2001: 285-295. [19] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L, et al. Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web, 2010: 811-820. [20] LI J, REN P, CHEN Z H, et al. Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017: 1419-1428. [21] WANG M, REN P, LEI M, et al. A collaborative session-based recommendation approach with parallel memory modules[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 345-354. [22] QIU R, LI J, HUANG Z, et al. Rethinking the item order in session-based recommendation with graph neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 579-588.
2022, Vol. 36 
