电子商务是数字经济产业中的重要应用领域. 在电商平台中用户与商品的交互会话中, 微观操作行为可以洞察用户细粒度的兴趣偏好. 同时, 宏观的多周期会话序列又可以反映用户兴趣的动态演化. 因此, 如何融合二者进行准确全面的用户兴趣表征, 进而开展精准推荐是当前的一个热点和难点问题. 深度循环神经网络RNN在处理周期性和长期依赖关系的序列数据方面具有突出的优势, 是当今人工智能AI的核心方法之一. 基于此, 在本文构建的 Mp-UIP模型框架中, 首先对用户会话中的微观行为进行细粒度的行为规律与兴趣学习. 其次, 设计分层的RNN网络结构: 会话级LSTMm ses、 区块级LSTMm blo 和用户级LSTMm usr, 分别学习用户短期、 中期以及长期的兴趣演变, 并进行多周期的兴趣融合. 最后, 在两个实际数据集上, 本文设计了模型消融、 基准模型对比、 稀疏性评价以及实际案例分析四方面的实验. 在考察推荐准确性和排名正确性的两个指标: Recall@K和MRR@K上, 实验结果验证提出的模型Mp-UIM较现有的经典模型表现最佳. 这表明提出的模型Mp-UIP将用户微观操作细节中的兴趣学习与宏观会话序列中的多周期兴趣演化相融合后, 能够构建出更加准确全面的用户兴趣模型, 服务于精准、 个性化的电子商务智能推荐服务.
Abstract
E-commerce is an important application field in the digital economy industry. In the interaction sessions between users and goods on the e-commerce platform, the micro-operation behaviors can gain insight into the user's fine-grained purchase interests, and at the same time, the macro multi-period session sequence can reflect the dynamic evolution of user's interests. Therefore, it has become a hot and difficult problem that how to integrate the two to conduct accurate and comprehensive user interest modeling, and then carry out accurate recommendation. The deep recurrent neural network (RNN) has outstanding advantages in processing serial data with periodic and long-term dependencies, and is one of the core methods of AI. Based on this, a hierarchical RNN network: Session-level LSTMm ses, block-level LSTMm blo and user-level LSTMm usr, is designed in the framework of the proposed model MpUIP. Firstly, the user's fine-grained interests are first learned from the micro behavior details in the session. Further, the evolution of user's short-term, medium-term and long-term interests are learned, and the multi-period interests are fused. Finally, on two real datasets, four experiments: model ablation, benchmark model comparison, sparsity evaluation, and real case analysis are conducted. On the two typical indicators of recommendation: Recall@K and MRR@K, the experimental results verify that the proposed model Mp-UIP has best performance than the existing classical models. This confirms that the model Mp-UIP can indeed build a more accurate and comprehensive user interest model by combining the user's fine-grained interests with the multi-period interest evolution representation, so as to serve for the accurate and personalized E-commerce recommendations.
关键词
电子商务 /
多周期用户兴趣 /
会话推荐 /
循环神经网络 /
行为细节
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Key words
e-commerce /
multi-period user interests /
session-based recommendation /
recurrent neural network /
behavior details
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参考文献
[1] 吴俊杰, 刘冠男, 王静远, 等. 数据智能:趋势与挑战[J]. 系统工程理论与实践, 2020, 40(8):2116-2149. Wu J J, Liu G N, Wang J Y, et al. Data intelligence:Trends and challenges[J]. Systems Engineering-Theory & Practice, 2020, 40(8):2116-2149.
[2] Zhu Z G, Zhou Y H, Deng X Y, et al. A graph-oriented model for hierarchical user interest in precision social marketing[J]. Electronic Commerce Research and Applications, 2019, 35:1-12.
[3] 朱志国, 周雨禾, 王谢宁. 移动商务中融合签到位置与用户间相似性的兴趣点精准推荐[J]. 系统工程理论与实践, 2020, 40(2):462-469. Zhu Z G, Zhou Y H, Wang X N. Recommendation of POI by integrating user similarity and location information in mobile commerce[J]. Systems Engineering-Theory & Practice, 2020, 40(2):462-469.
[4] 张麒, 吴宾, 孙中川, 等. 细粒度建模用户兴趣的序列化推荐方法[J]. 中国科学:信息科学, 2022, 52(10):1775-1791. Zhang Q, Wu B, Sun Z C, et al. Fine-grained modeling of user interests for sequential recommendation[J]. Science in China (Information Sciences), 2022, 52(10):1775-1791.
[5] Zhu H, Li X, Zhang P, et al. Learning tree-based deep model for recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018:1079-1088.
[6] 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, 33(1):5941-5948.
[7] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018:1059-1068.
[8] Bai T, Zou L, Zhao W X, et al. CTRec:A long-short demands evolution model for continuous-time recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019:675-684.
[9] Huang C, Wu X, Zhang X, et al. Online purchase prediction via multi-scale modeling of behavior dynamics[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019:2613-2622.
[10] Zhao X, Xia L, Tang J, et al. "Deep reinforcement learning for search, recommendation, and online advertising:A survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator[J]. ACM Sigweb Newsletter, 2019:1-15. doi:10.1145/3320496.3320500.
[11] Zou L, Xia L, Ding Z, et al. Reinforcement learning to optimize long-term user engagement in recommender systems[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019:2810-2818.
[12] Li Z H, Wang X Z, Yang C, et al. Exploiting explicit and implicit item relationships for session-based recommendation[C]//Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023.
[13] 陈聪, 张伟, 王骏. 带有时间预测辅助任务的会话式序列推荐[J]. 计算机学报, 2021, 44(9):1841-1853. Chen C, Zhang W, Wang J. Session-based sequential recommendation with auxiliary time prediction[J]. Chinese Journal of Computers, 2021, 44(9):1841-1853.
[14] Wang S, Cao L, Wang Y, et al. A survey on session-based recommender systems[J]. ACM Computing Surveys (CSUR), 2021, 54(7):1-38.
[15] Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-based recommendations with recurrent neural networks[J]. arXiv preprint arXiv:1511.06939, 2015.
[16] Quadrana M, Karatzoglou A, Hidasi B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems-RecSys'17. New York:ACM Press, 2017:130-137.
[17] Ruocco M, Skrede O S L, Langseth H. Inter-session modeling for session-based recommendation[C]//Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, 2017:24-31.
[18] He X, Zhang H, Kan M Y, et al. Fast matrix factorization for online recommendation with implicit feedback[C]//Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016:549-558.
[19] Zhao W, Wang B, Ye J, et al. Plastic:Prioritize long and short-term information in top-n recommendation using adversarial training[C]//IJCAI. Stockholm:IJCAI Organization, 2018:3676-3682.
[20] 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.
[21] Jannach D, Ludewig M. When recurrent neural networks meet the neighborhood for session-based recommendation[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017:306-310.
[22] Zhao Z, Cheng Z, Hong L, et al. Improving user topic interest profiles by behavior factorization[C]//Proceedings of the 24th International Conference on World Wide Web, 2015:1406-1416.
[23] Zhang W, Zhang X, Wang H, et al. A deep variational matrix factorization method for recommendation on large scale sparse dataset[J]. Neurocomputing, 2019, 334:206-218.
[24] Ran W. Movie recommendation model of neural collaborative filtering based on multi attribute fusion[J]. Computer Science and Application, 2023. doi:10.12677/csa.2023.133030.
[25] Barkan O, Koenigstein N. Item2vec:Neural item embedding for collaborative filtering[C]//2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE, 2016:1-6.
[26] Wu C Y, Ahmed A, Beutel A, et al. Recurrent recommender networks[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017:495-503.
[27] Zhu Y, Li H, Liao Y, et al. What to do next:Modeling user behaviors by Time-LSTM[C]//IJCAI, 2017, 17:3602-3608.
[28] Wan M, McAuley J. Item recommendation on monotonic behavior chains[C]//Proceedings of the 12th ACM Conference on Recommender Systems, 2018:86-94.
[29] Zhou M, Ding Z, Tang J, et al. Micro behaviors:A new perspective in e-commerce recommender systems[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018:727-735.
[30] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, 2016:1149-1154.
[31] Hidasi B, Quadrana M, Karatzoglou A, et al. Parallel recurrent neural network architectures for feature-rich session-based recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems, 2016:241-248.
[32] Tan Y K, Xu X, Liu Y. Improved recurrent neural networks for session-based recommendations[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016:17-22.
[33] Chatzis S P, Christodoulou P, Andreou A S. Recurrent latent variable networks for session-based recommendation[C]//Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, 2017:38-45.
[34] Jannach D, Ludewig M. When recurrent neural networks meet the neighborhood for session-based recommendation[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017:306-310.
[35] Oh E, Kim T, Kim M, et al. SR-GCL:Session-based recommendation with global context enhanced augmentation in contrastive learning[C]//DLG-AAAI'22, 2022, ArXiv, abs/2209.10807.
[36] Twardowski B. Modelling contextual information in session-aware recommender systems with neural networks[C]//Proceedings of the 10th ACM Conference on Recommender Systems, 2016:273-276.
[37] Jing H, Smola A J. Neural survival recommender[C]//Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017:515-524.
[38] Zhou M, Ding Z, Tang J, et al. Micro behaviors:A new perspective in e-commerce recommender systems[C]//Proceedings of the Eleventh Acm International Conference on Web Search and Data Mining, 2018:727-735.
[39] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
[40] Rendle S, Freudenthaler C, Gantner Z, et al. BPR:Bayesian personalized ranking from implicit feedback[J]. arXiv preprint arXiv:1205.2618, 2012.
[41] Hidasi B, Karatzoglou A. Recurrent neural networks with top-k gains for session-based recommendations[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018:843-852.
[42] Clevert D A, Unterthiner T, Hochreiter S. Fast and accurate deep network learning by exponential linear units (ELUs)[J]. arXiv preprint arXiv:1511.07289, 2015.
[43] Kingma D P, Ba J. Adam:A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.
[44] Cremonesi P, Koren Y, Turrin R. Performance of recommender algorithms on top-n recommendation tasks[C]//Proceedings of the Fourth ACM Conference on Recommender Systems, 2010:39-46.
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基金
国家自然科学基金面上项目（72172025，72101051）；教育部人文社科规划基金（21YJAZH130）；2022年度辽宁省教育厅基本科研面上项目（LJKMZ20221606）
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