博碩士論文 110423015 詳細資訊




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姓名 周亭妏(Ting-Wen Chou)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 利用圖卷積網路的時間感知客戶購買意圖預測
(Time-Aware Customer Purchase Intention Prediction with Graph Convolutional Networks)
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摘要(中) 深度學習模型被廣泛應用於處理序列資料,例如用戶購買歷史,以預測可能的購買項目。這些模型可以考慮複雜的非線性和動態關係,並且能夠捕捉用戶的長期序列行為。然而,對企業而言,現有的深度學習推薦模型對企業來說是不夠直觀且方便的;他們通常只是希望知道用戶是否會購買某項商品以及何時會購買。
本文是第一個提出決策模型以回答商家關心的資訊的研究:在特定時間內,使用者是否會購買某個商品。該模型基於消費者過去按時間順序購買商品的序列資料,以及商品被消費者依序購買的序列資料,採用圖形神經網路來捕捉使用者和商品之間的高階相關性,使用Transformer來處理序列間的依賴關係,並使用GRU來處理序列中不同時間元素之間的動態關係。相較於過去的序列推薦模型,此模式可以讓商家了解對於它所關注的重點商品和客戶,是否有可能產生有效的購買。
摘要(英) Deep learning models have been widely applied to handle sequential data, such as customer purchase history, to predict potential purchases. These models can consider complex non-linear and dynamic relationships and capture users′ long-term sequential behaviors. However, for businesses, existing deep learning recommendation models may not be intuitive and convenient enough. They often simply want to know whether a user will make a purchase on specific items and when it is likely to occur.
This paper presents the first decision model to address the information of interest to businesses: answering whether a user will purchase a specific item within a given time frame. The model is based on sequential data of users′ past purchases and the sequential data of items’ past purchases by users. It utilizes graph convolutional networks to capture high-order correlations between users and items, uses Transformers to handle dependencies among sequences, and utilizes GRU (Gated Recurrent Unit) to capture dynamic relationships between different time elements in the sequences. Compared to previous sequential recommendation models, this approach allows businesses to gain insights into the potential effectiveness of purchases for their key items and customers of interest.
關鍵字(中) ★ 序列推薦
★ 深度學習
★ 神經網路
★ 圖卷積網路
★ Transformer
關鍵字(英) ★ Sequential Recommendation
★ Deep Learning
★ Neural Network
★ Graph Convolutional Networks
★ Transformer
論文目次 摘要 i
List of Figures iv
1. Introduction 1
2. Related work 6
2-1 Markov Chain-Based Methods 6
2-2 Recurrent Neural Networks (RNN)-Based Methods 7
2-3 Graph Neural Networks (GNN)-Based Methods 7
2-4 Transformer-Based Methods 9
2-5 Conclusion 9
3. Proposed approach 11
3-1 Problem Definition 11
3-2 Model Architecture 11
3-2-1 LightGCN 13
3-2-2 Transformer Encoder 15
3-2-3 Gated Recurrent Unit (GRU) Pooling 18
3-3 Prediction and Loss Function 19
4. Experiments 21
4-1 Datasets 21
4-2 Baselines and comparison method 21
5. Conclusion 25
Reference 26
參考文獻 [1] Z. Li, H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen, “Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’18. New York, NY, USA: Association for Computing Machinery, Jul. 2018, pp. 1734–1743. doi: 10.1145/3219819.3220014.
[2] F. Yuan, A. Karatzoglou, I. Arapakis, J. M. Jose, and X. He, “A Simple Convolutional Generative Network for Next Item Recommendation,” in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne VIC Australia: ACM, Jan. 2019, pp. 582–590. doi: 10.1145/3289600.3290975.
[3] X. Peng, S. Huang, and Z. Niu, “An Study on Personalized Recommendation Model Based on Search Behaviors and Resource Properties,” in 2010 2nd International Conference on Information Engineering and Computer Science, Feb. 2010, pp. 1–4. doi: 10.1109/ICIECS.2010.5678283.
[4] S.-J. Hong, S.-K. Lee, and S.-I. Yang, “Champion Recommendation System of League of Legends,” in 2020 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2020, pp. 1252–1254. doi: 10.1109/ICTC49870.2020.9289546.
[5] S. Zhang, H. Liu, J. He, S. Han, and X. Du, “A deep bi-directional prediction model for live streaming recommendation,” Information Processing & Management, vol. 58, no. 2, p. 102453, Mar. 2021, doi: 10.1016/j.ipm.2020.102453.
[6] S. Saini, S. Saumya, and J. P. Singh, “Sequential Purchase Recommendation System for E-Commerce Sites,” in Computer Information Systems and Industrial Management, K. Saeed, W. Homenda, and R. Chaki, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017, pp. 366–375. doi: 10.1007/978-3-319-59105-6_31.
[7] M. Zhang, Y. Yang, R. Abbas, K. Deng, J. Li, and B. Zhang, “SNPR: A Serendipity-Oriented Next POI Recommendation Model,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event Queensland Australia: ACM, Oct. 2021, pp. 2568–2577. doi: 10.1145/3459637.3482394.
[8] M. Schedl, “Deep Learning in Music Recommendation Systems,” Frontiers in Applied Mathematics and Statistics, vol. 5, 2019, Accessed: Oct. 10, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fams.2019.00044
[9] Q. Wu, Y. Gao, X. Gao, P. Weng, and G. Chen, “Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage AK USA: ACM, Jul. 2019, pp. 447–457. doi: 10.1145/3292500.3330959.
[10] S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme, “Factorizing personalized Markov chains for next-basket recommendation,” in Proceedings of the 19th international conference on World wide web - WWW ’10, Raleigh, North Carolina, USA: ACM Press, 2010, p. 811. doi: 10.1145/1772690.1772773.
[11] N. Lathia, S. Hailes, L. Capra, and X. Amatriain, “Temporal diversity in recommender systems,” in Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’10, Geneva, Switzerland: ACM Press, 2010, p. 210. doi: 10.1145/1835449.1835486.
[12] R. He and J. McAuley, “Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation,” in 2016 IEEE 16th International Conference on Data Mining (ICDM), Feb. 2016, pp. 191–200. doi: 10.1109/ICDM.2016.0030.
[13] J. Huang, W. X. Zhao, H. Dou, J.-R. Wen, and E. Y. Chang, “Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor MI USA: ACM, Jun. 2018, pp. 505–514. doi: 10.1145/3209978.3210017.
[14] Q. Liu, S. Wu, D. Wang, Z. Li, and L. Wang, “Context-Aware Sequential Recommendation,” in 2016 IEEE 16th International Conference on Data Mining (ICDM), Feb. 2016, pp. 1053–1058. doi: 10.1109/ICDM.2016.0135.
[15] Q. Cui, S. Wu, Q. Liu, W. Zhong, and L. Wang, “MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 2, pp. 317–331, Feb. 2020, doi: 10.1109/TKDE.2018.2881260.
[16] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: a deep learning approach for short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, 2017, doi: 10.1049/iet-its.2016.0208.
[17] C. Ma, L. Ma, Y. Zhang, J. Sun, X. Liu, and M. Coates, “Memory Augmented Graph Neural Networks for Sequential Recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, Art. no. 04, Apr. 2020, doi: 10.1609/aaai.v34i04.5945.
[18] J. Chang et al., “Sequential Recommendation with Graph Neural Networks,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event Canada: ACM, Jul. 2021, pp. 378–387. doi: 10.1145/3404835.3462968.
[19] R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph Convolutional Neural Networks for Web-Scale Recommender Systems,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’18. New York, NY, USA: Association for Computing Machinery, Jul. 2018, pp. 974–983. doi: 10.1145/3219819.3219890.
[20] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural Graph Collaborative Filtering,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris France: ACM, Jul. 2019, pp. 165–174. doi: 10.1145/3331184.3331267.
[21] R. van den Berg, T. N. Kipf, and M. Welling, “Graph Convolutional Matrix Completion.” arXiv, Oct. 25, 2017. Accessed: Oct. 17, 2022. [Online]. Available: http://arxiv.org/abs/1706.02263
[22] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China: ACM, Jul. 2020, pp. 639–648. doi: 10.1145/3397271.3401063.
[23] W.-C. Kang and J. McAuley, “Self-Attentive Sequential Recommendation,” in 2018 IEEE International Conference on Data Mining (ICDM), Jan. 2018, pp. 197–206. doi: 10.1109/ICDM.2018.00035.
[24] F. Sun et al., “BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer.” arXiv, Aug. 21, 2019. Accessed: Oct. 10, 2022. [Online]. Available: http://arxiv.org/abs/1904.06690
[25] J. Li, Y. Wang, and J. McAuley, “Time Interval Aware Self-Attention for Sequential Recommendation,” in Proceedings of the 13th International Conference on Web Search and Data Mining, Houston TX USA: ACM, Jan. 2020, pp. 322–330. doi: 10.1145/3336191.3371786.
[26] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4–24, Jan. 2021, doi: 10.1109/TNNLS.2020.2978386.
[27] S. Aksoy, T. G. Kolda, and A. Pinar, “Measuring and Modeling Bipartite Graphs with Community Structure,” p. 24.
[28] D. Mei, N. Huang, and X. Li, “Light Graph Convolutional Collaborative Filtering With Multi-Aspect Information,” IEEE Access, vol. 9, pp. 34433–34441, 2021, doi: 10.1109/ACCESS.2021.3061915.
[29] R. Ragesh, S. Sellamanickam, V. Lingam, A. Iyer, and R. Bairi, “User Embedding based Neighborhood Aggregation Method for Inductive Recommendation.” arXiv, Feb. 16, 2021. Accessed: Oct. 24, 2022. [Online]. Available: http://arxiv.org/abs/2102.07575
[30] Y. Shen et al., “How Powerful is Graph Convolution for Recommendation?,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event Queensland Australia: ACM, Oct. 2021, pp. 1619–1629. doi: 10.1145/3459637.3482264.
[31] Q. Chen, H. Zhao, W. Li, P. Huang, and W. Ou, “Behavior sequence transformer for e-commerce recommendation in Alibaba,” in Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, Anchorage Alaska: ACM, Aug. 2019, pp. 1–4. doi: 10.1145/3326937.3341261.
[32] A. Vaswani et al., “Attention is All you Need,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2017. Accessed: Oct. 10, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
[33] J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer Normalization.” arXiv, Jul. 21, 2016. Accessed: Oct. 31, 2022. [Online]. Available: http://arxiv.org/abs/1607.06450
[34] B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, “Session-based Recommendations with Recurrent Neural Networks.” arXiv, Mar. 29, 2016. Accessed: Nov. 18, 2022. [Online]. Available: http://arxiv.org/abs/1511.06939
[35] R. Dey and F. M. Salem, “Gate-variants of Gated Recurrent Unit (GRU) neural networks,” in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Aug. 2017, pp. 1597–1600. doi: 10.1109/MWSCAS.2017.8053243.
[36] P. Wang, J. Guo, Y. Lan, J. Xu, S. Wan, and X. Cheng, “Learning Hierarchical Representation Model for NextBasket Recommendation,” in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago Chile: ACM, Aug. 2015, pp. 403–412. doi: 10.1145/2766462.2767694.
[37] F. Yu, Q. Liu, S. Wu, L. Wang, and T. Tan, “A Dynamic Recurrent Model for Next Basket Recommendation,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa Italy: ACM, Jul. 2016, pp. 729–732. doi: 10.1145/2911451.2914683.
[38] D.-T. Le, H. W. Lauw, and Y. Fang, “Correlation-Sensitive Next-Basket Recommendation,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China: International Joint Conferences on Artificial Intelligence Organization, Aug. 2019, pp. 2808–2814. doi: 10.24963/ijcai.2019/389.
指導教授 陳彥良(Yen-Liang Chen) 審核日期 2023-8-17
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