利用门控网络构建用户动态兴趣的序列推荐模型

doi:10.19678/j.issn.1000-3428.0062184

摘要/Abstract

摘要： 在推荐系统中，现有多数序列推荐方法将用户行为视为一个时间有序的序列进行用户兴趣建模，用户兴趣的动态变化导致模型难以从用户行为序列中捕捉准确的用户兴趣信息。针对该问题，同时考虑到项目间成对的共现模式应作为交互信息的补充，提出利用门控网络构建用户动态兴趣的序列推荐模型DCGN。使用门控线性单元捕获交互序列中的用户兴趣，利用带有注意力权重的门控循环网络学习用户的动态兴趣。在此基础上，对用户交互项目间的共现模式进行建模，与用户兴趣信息以及用户信息进行融合后输入深度神经网络，得到最终推荐结果。在ML100K、Amazon 5-Elect、Retailrocket 3个公开数据集上进行实验，使用精确率、归一化折损累积增益和命中率进行性能评估，结果表明，DCGN模型较NARM、GRU4Rec、NLR等主流序列推荐模型性能更优，其归一化折损增益和精确率在Retailrocket数据集上平均提升1.9%和1.22%，在Amazon 5-Elect数据集上平均提升0.82%和1.05%，在ML100K数据集上平均提升0.36%和0.31%。

Abstract: In recommendation systems, most existing sequence recommendation methods regard user behavior as a time-ordered sequence to model user interest.The dynamic change of user interest, however, makes it difficult for the model to capture accurate user interest information from the user behavior sequence.Aiming at solving this problem, and considering that the paired co-occurrence pattern between items should be used as a supplement to interaction information, a sequential recommendation model DCGN is proposed to construct user's dynamic interests by using gated network.A Gated Linear Unit(GLU) is used to capture user interests in interaction sequences, and a gated recurrent network with attention weights is used to learn the user's dynamic interests.In this way the co-occurrence pattern between user interaction items is modeled, and the user interest information and user information are integrated into the deep neural network to obtain the final recommendation result.Experiments are carried out on three public datasets, ML100K, Amazon 5-Elect, and Retailrocket, and the performance is evaluated in terms of the following three metrics:precision, Normalized Depreciation Cumulative Gain(NDCG) and hit rate.The results show that the DCGN model has better performance compared with the mainstream sequence recommendation models such as NARM, GRU4Rec, NLR, et al.Its NDCG and precision increase of 1.9% and 1.22% on Retailrocket dataset, 0.82% and 1.05% on Amazon 5-Elect dataset and 0.36% and 0.31% on ML100K dataset, in average.

Key words: recommendation algorithm, attention mechanism, Gated Linear Unit(GLU), project co-occurrence mode, dynamic interest

中图分类号:

TP391.3

王燕, 范林, 赵妮妮. 利用门控网络构建用户动态兴趣的序列推荐模型[J]. 计算机工程, 2022, 48(8): 283-291.

WANG Yan, FAN Lin, ZHAO Nini. Sequential Recommendation Model Using Gated Network to Construct User's Dynamic Interest[J]. Computer Engineering, 2022, 48(8): 283-291.

http://www.ecice06.com/CN/Y2022/V48/I8/283

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参考文献

[1] SUN J N, GUO W, ZHANG D C, et al.A framework for recommending accurate and diverse items using Bayesian graph convolutional neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining.New York, USA:ACM Press, 2020:2030-2039.
[2] SUN K, QIAN T Y, CHEN T, et al.Where to go next:modeling long- and short-term user preferences for point-of-interest recommendation[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.Palo Alto, USA:AAAI Press, 2020:214-221.
[3] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[EB/OL].[2021-06-11].https://arxiv.org/abs/1706.03762.
[4] LIU D H, WU J, LI J, et al.Adaptive hierarchical attention-enhanced gated network integrating reviews for item recommendation[J].IEEE Transactions on Knowledge and Data Engineering, 2022, 34(5):2076-2090.
[5] MA S C, ZHU J H.Self-attention based collaborative neural network for recommendation[M]//BIAGIONI E S, ZHENG Y, CHENG S Y.Wireless algorithms, systems, and applications.Berlin, Germany:Springer, 2019:235-246.
[6] CHENG C, YANG H Q, LYU M R, et al.Where you like to go next:successive point-of-interest recommendation[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.Washington D.C., USA:IEEE Press, 2013:2605-2611.
[7] PASRICHA R, McAULEY J.Translation-based factorization machines for sequential recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.New York, USA:ACM Press, 2018:63-71.
[8] LIU L, ZHANG P.A novel recommendation algorithm with knowledge graph[J].Journal of Physics:Conference Series, 2021, 1812(1):1-10.
[9] MA C, KANG P, LIU X.Hierarchical gating networks for sequential recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining.NewYork, USA:ACM Press, 2019:825-833.
[10] NI Y B, OU D, LIU S C, et al.Perceive your users in depth:learning universal user representations from multiple E-commerce tasks[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining.New York, USA:ACM Press, 2018:596-605.
[11] GU Y L, DING Z Y, WANG S Q, et al.Hierarchical user profiling for E-commerce recommender systems[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.Washington D.C., USA:IEEE Press, 2020:223-231.
[12] FENG Y F, LÜF Y, SHEN W C, et al.Deep session interest network for click-through rate prediction[EB/OL].[2021-06-11].https://arxiv.org/abs/1905.06482.
[13] CEN Y K, ZHANG J W, ZOU X, et al.Controllable multi-interest framework for recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining.New York, USA:ACM Press, 2020:2942-2951.
[14] DAUPHIN Y N, FAN A, AULI M, et al.Language modeling with gated convolutional networks[EB/OL].[2021-06-11].https://arxiv.org/abs/1612.08083.
[15] NING X, DESROSIERS C, KARYPIS G.A comprehensive survey of neighborhood-based recommendation methods[M]//RICCI F, ROKACH L, SHAPIRA B.Recommender systems handbook.Berlin, Germany:Springer, 2015:37-76.
[16] HARPER F M, KONSTAN J A.The MovieLens datasets:history and context[J].ACM Transactions on Interactive Intelligent Systems, 2016, 5(4):19.
[17] Amazon ecommerce dataset[EB/OL].[2021-06-11].http://jmcauley.ucsd.edu/data/amazon/links.html.
[18] Retailrocket[EB/OL].[2021-06-11].http://www.kaggle.com/retailrocket/ecommerce-dataset.
[19] LI J, REN P J, CHEN Z M, et al.Neural attentive session-based recommendation[C]//Proceedings of 2017 ACM Conference on Information and Knowledge Management.New York, USA:ACM Press, 2017:1419-1428.
[20] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al.Session-based recommendations with recurrent neural networks[EB/OL].[2021-06-11].https://arxiv.org/abs/1511.06939.
[21] LIU Q, ZENG Y F, MOKHOSI R, et al.STAMP:short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining.New York, USA:ACM Press, 2018:1831-1839.
[22] CHEN H X, SHI S Y, LI Y Q, et al.Neural collaborative reasoning[EB/OL].[2021-06-11].https://arxiv.org/abs/2005.08129.
[23] KANG W C, MCAULEY J.Self-attentive sequential recommendation[C]//Proceedings of IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2018:197-206.
[24] TANG J X, WANG K.Personalized top-N sequential recommendation via convolutional sequence embedding[C]//Proceedings of the 11th ACM International Conference on Web Search and Data Mining.New York, USA:ACM Press, 2018:565-573.
[25] XIAO J, YE H, HE X N, et al.Attentional factorization machines:learning the weight of feature interactions via attention networks[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne, Australia:[s.n.], 2017:1-10.

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