搭配籃子大小的下一個購物籃推薦

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：105

、訪客IP：3.148.108.201

姓名

郭羿德(Yi-Te Kuo) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

搭配籃子大小的下一個購物籃推薦
(Next Basket Recommendation with Basket Size Prediction)

相關論文

★ 零售業商業智慧之探討	★ 有線電話通話異常偵測系統之建置
★ 資料探勘技術運用於在學成績與學測成果分析 -以高職餐飲管理科為例	★ 利用資料採礦技術提昇財富管理效益 -以個案銀行為主
★ 晶圓製造良率模式之評比與分析－以國內某DRAM廠為例	★ 商業智慧分析運用於學生成績之研究
★ 運用資料探勘技術建構國小高年級學生學業成就之預測模式	★ 應用資料探勘技術建立機車貸款風險評估模式之研究－以A公司為例
★ 績效指標評估研究應用於提升研發設計品質保證	★ 基於文字履歷及人格特質應用機械學習改善錄用品質
★ 以關係基因演算法為基礎之一般性架構解決包含限制處理之集合切割問題	★ 關聯式資料庫之廣義知識探勘
★ 考量屬性值取得延遲的決策樹建構	★ 從序列資料中找尋偏好圖的方法 - 應用於群體排名問題
★ 利用分割式分群演算法找共識群解群體決策問題	★ 以新奇的方法有序共識群應用於群體決策問題

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2027-7-1以後開放)

摘要(中)

隨著電子商務的迅速發展，消費者雖然能獲得日益增多的商品選擇，不過這也同時導致他們面臨訊息過載的挑戰，而推薦系統的出現能有效緩解這個問題。推薦系統可以進一步細分為不同的推薦任務，其中，下一個購物籃推薦任務主要是從用戶的購物籃歷史序列資料中，捕捉用戶的購買偏好，進而為用戶推薦下一個購物籃的商品。然而，過去的購物籃推薦方法大多僅在學習用戶的購買歷史後，直接向用戶推薦相同大小的下一個購物籃，卻忽略了不同用戶可能因消費習慣或經濟能力，而導致籃子大小有所不同。因此，本研究提出了一種搭配籃子大小的下一個購物籃推薦模型NBR-WBS。此模型先是透過注意力機制與長短期記憶分別為購物籃與購物籃大小進行編碼，接著將兩者的嵌入一起輸入到Transformer中進行多任務學習，以獲得購物籃的最終狀態向量。隨後，我們將該向量輸入到各別組件中進行特定任務的預測，並根據兩項任務的預測結果，為用戶進行動態大小的購物籃推薦。在本研究中，我們對三個真實世界的資料集進行了實驗，結果顯示我們所提出的方法在推薦性能上優於其他在下一個購物籃推薦任務的現有方法。

摘要(英)

With the rapid development of e-commerce, consumers benefit from an increasing variety of products but also face the challenge of information overload. Recommendation systems can effectively address this issue by helping users filter through the abundance of choices. These systems include various tasks, such as session-based recommendation, sequential recommendation, and the next basket recommendation task. Among them, the next basket recommendation task aims to capture users′ purchasing preferences from their historical basket sequences and recommend items for their next basket.
However, most of the previous basket recommendation methods merely recommend the same size basket based on the user′s purchase history, ignoring that different users may have varying basket sizes due to consumption habits or economic capability. Therefore, in this study, we propose a next basket recommendation model that incorporates basket size prediction, named NBR-WBS (Next Basket Recommendation with Basket Size). This model first encodes the baskets and basket sizes separately using an attention mechanism and Long Short-Term Memory (LSTM), respectively. The embeddings of both are then input into a Transformer for multi-task learning (MTL) to obtain the final state vector of the basket. Subsequently, we input this vector into specific components for task prediction. Based on the results of these two tasks, we recommend dynamically sized baskets to users. Our experiments on three real-world datasets demonstrate that our proposed method outperforms existing methods regarding recommendation performance for the next basket recommendation task.

關鍵字(中)

★ 下一個購物籃推薦
★ 多任務學習
★ Transformer
★ 動態購物籃大小

關鍵字(英)

★ Next Basket Recommendation
★ Multi-task Learning
★ Transformer
★ Dynamic Basket Size

論文目次

摘要 ............................................................................................................................................. i
ABSTRACT ............................................................................................................................... ii
List of Figures ............................................................................................................................. v
List of Tables ............................................................................................................................. vi
1. INTRODUCTION .................................................................................................................. 1
1.1 Research Background ................................................................................................... 1
1.2 Research Motivation ..................................................................................................... 3
1.3 Research Purpose .......................................................................................................... 4
2. RELATED WORK ................................................................................................................. 7
2.1 Traditional Methods ...................................................................................................... 7
2.1.1 Markov Chains .................................................................................................. 7
2.1.2 Collaborative Filtering Methods ........................................................................ 7
2.1.3 Hybrid Methods ................................................................................................. 8
2.2 Neural Network Methods ............................................................................................. 9
2.2.1 RNN-based Methods ....................................................................................... 10
2.2.2 Transformer ..................................................................................................... 10
2.2.3 GNN ................................................................................................................ 11
2.3 Finding Optimal Hyperparameters ............................................................................. 11
2.4 Summary ..................................................................................................................... 12
3. METHODOLOGY ............................................................................................................... 14
3.1 Problem Definition and Notation Explanation ........................................................... 14
3.2 Model Overview ......................................................................................................... 15
3.3 Embedding Module .................................................................................................... 17
3.3.1 Basket Encoder ................................................................................................ 17
3.3.2 Basket-Size Encoder ........................................................................................ 20
3.4 Basket Preference Learning Module .......................................................................... 21
3.5 Prediction Module ...................................................................................................... 25
3.5.1 Basket Predictor ............................................................................................... 25
3.5.2 Basket-Size Predictor ...................................................................................... 27
3.6 Loss Function ............................................................................................................. 27
4. EXPERIMENTS AND RESULTS ....................................................................................... 29
4.1 Datasets ....................................................................................................................... 29
4.2 Baselines ..................................................................................................................... 30
4.3 Evaluation Metrics ...................................................................................................... 31
4.4 Experimental Setup .................................................................................................... 34
4.5 Performance Comparison ........................................................................................... 35
4.6 Sensitivity analysis ..................................................................................................... 37
4.7 Ablation Study ............................................................................................................ 43
5. CONCLUSION AND FUTURE WORK ............................................................................. 45
5.1 Conclusion .................................................................................................................. 45
5.2 Future Work ................................................................................................................ 46
REFERENCES ......................................................................................................................... 48
APPENDIX .............................................................................................................................. 53

參考文獻

[1] Z. Zamanzadeh Darban and M. H. Valipour, “GHRS: Graph-based hybrid recommendation system with application to movie recommendation,” Expert Systems with Applications, vol. 200, p. 116850, Aug. 2022, doi: 10.1016/j.eswa.2022.116850.
[2] C. Hansen, C. Hansen, L. Maystre, R. Mehrotra, B. Brost, F. Tomasi, and M. Lalmas, “Contextual and Sequential User Embeddings for Large-Scale Music Recommendation,” in Proceedings of the 14th ACM Conference on Recommender Systems, in RecSys ’20. New York, NY, USA: Association for Computing Machinery, Sep. 2020, pp. 53–62. doi: 10.1145/3383313.3412248.
[3] G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “DRN: A Deep Reinforcement Learning Framework for News Recommendation,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18, Lyon, France: ACM Press, 2018, pp. 167–176. doi: 10.1145/3178876.3185994.
[4] X. Li, X. Wang, X. He, L. Chen, J. Xiao, and T.-S. Chua, “Hierarchical Fashion Graph Network for Personalized Outfit Recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event China: ACM, Jul. 2020, pp. 159–168. doi: 10.1145/3397271.3401080.
[5] P. Nitu, J. Coelho, and P. Madiraju, “Improvising personalized travel recommendation system with recency effects,” Big Data Mining and Analytics, vol. 4, no. 3, pp. 139–154, Sep. 2021, doi: 10.26599/BDMA.2020.9020026.
[6] J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, “Neural Attentive Session-based Recommendation,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, in CIKM ’17. New York, NY, USA: Association for Computing Machinery, Nov. 2017, pp. 1419–1428. doi: 10.1145/3132847.3132926.
[7] S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-Based Recommendation with Graph Neural Networks,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, Art. no. 01, Jul. 2019, doi: 10.1609/aaai.v33i01.3301346.
[8] F. Yu, Y. Zhu, Q. Liu, S. Wu, L. Wang, and T. Tan, “TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’20. New York, NY, USA: Association for Computing Machinery, Jul. 2020, pp. 1921–1924. doi: 10.1145/3397271.3401319.
[9] 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.
[10] F. Sun, J. Liu, J. Wu, C. Pei, X. Lin, W. Ou, and P. Jiang, “BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management, in CIKM ’19. New York, NY, USA: Association for Computing Machinery, Nov. 2019, pp. 1441–1450. doi: 10.1145/3357384.3357895.
[11] 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, in WWW ’10. New York, NY, USA: Association for Computing Machinery, Apr. 2010, pp. 811–820. doi: 10.1145/1772690.1772773.
[12] H. Hu, X. He, J. Gao, and Z.-L. Zhang, “Modeling Personalized Item Frequency Information for Next-basket Recommendation,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’20. New York, NY, USA: Association for Computing Machinery, Jul. 2020, pp. 1071–1080. doi: 10.1145/3397271.3401066.
[13] Y. Qin, P. Wang, and C. Li, “The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’21. New York, NY, USA: Association for Computing Machinery, Jul. 2021, pp. 859–868. doi: 10.1145/3404835.3462836.
[14] G. Faggioli, M. Polato, and F. Aiolli, “Recency Aware Collaborative Filtering for Next Basket Recommendation,” in Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, in UMAP ’20. New York, NY, USA: Association for Computing Machinery, Jul. 2020, pp. 80–87. doi: 10.1145/3340631.3394850.
[15] 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, in SIGIR ’15. New York, NY, USA: Association for Computing Machinery, Aug. 2015, pp. 403–412. doi: 10.1145/2766462.2767694.
[16] 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, in SIGIR ’16. New York, NY, USA: Association for Computing Machinery, Jul. 2016, pp. 729–732. doi: 10.1145/2911451.2914683.
[17] D.-T. Le, H. W. Lauw, and Y. Fang, “Correlation-Sensitive Next-Basket Recommendation,” presented at the the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macau, China, Aug. 2019. Accessed: Sep. 22, 2023. [Online]. Available: https://www.ijcai.org/proceedings/2019/389
[18] G. Shani, D. Heckerman, and R. I. Brafman, “An MDP-based recommender system,” Journal of Machine Learning Research, vol. 6, 2005.
[19] W. Gu, S. Dong, and Z. Zeng, “Increasing recommended effectiveness with markov chains and purchase intervals,” Neural Comput & Applic, vol. 25, no. 5, pp. 1153–1162, Oct. 2014, doi: 10.1007/s00521-014-1599-8.
[20] R. He and J. McAuley, “Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation,” Sep. 2016, doi: 10.48550/arXiv.1609.09152.
[21] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Jan. 2013, doi: 10.48550/arXiv.1301.7363.
[22] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th international conference on World Wide Web, Hong Kong Hong Kong: ACM, Apr. 2001, pp. 285–295. doi: 10.1145/371920.372071.
[23] O. Barkan and N. Koenigstein, “ITEM2VEC: Neural item embedding for collaborative filtering,” in 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Salerno, Italy: IEEE, Sep. 2016, pp. 1–6. doi: 10.1109/MLSP.2016.7738886.
[24] M. SALAMPASIS, T. SIOMOS, A. KATSALIS, K. DIAMANTARAS, K. CHRISTANTONIS, M. DELIANIDI, and I. KARAVELI, “Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations,” in Proceedings of the 2021 13th International Conference on Machine Learning and Computing, in ICMLC ’21. New York, NY, USA: Association for Computing Machinery, Jun. 2021, pp. 477–484. doi: 10.1145/3457682.3457755.
[25] Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, in ICML’14. Beijing, China: JMLR.org, Jun. 2014, p. II-1188-II–1196.
[26] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009, doi: 10.1109/MC.2009.263.
[27] H. Wang, “ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data,” in 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), Sep. 2021, pp. 102–105. doi: 10.1109/ICISCAE52414.2021.9590668.
[28] H. Wang, “PoissonMat: Remodeling Matrix Factorization using Poisson Distribution and Solving the Cold Start Problem without Input Data,” in 2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), Aug. 2022, pp. 245–249. doi: 10.1109/MLISE57402.2022.00055.
[29] N. P. Kumar and Z. Fan, “Hybrid User-Item Based Collaborative Filtering,” Procedia Computer Science, vol. 60, pp. 1453–1461, Jan. 2015, doi: 10.1016/j.procs.2015.08.222.
[30] T. Bai, J.-Y. Nie, W. X. Zhao, Y. Zhu, P. Du, and J.-R. Wen, “An Attribute-aware Neural Attentive Model for Next Basket Recommendation,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor MI USA: ACM, Jun. 2018, pp. 1201–1204. doi: 10.1145/3209978.3210129.
[31] H. Hu and X. He, “Sets2Sets: Learning from Sequential Sets with Neural Networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage AK USA: ACM, Jul. 2019, pp. 1491–1499. doi: 10.1145/3292500.3330979.
[32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, in NIPS’17. Red Hook, NY, USA: Curran Associates Inc., Dec. 2017, pp. 6000–6010.
[33] J. Yang, J. Xu, J. Tong, S. Gao, J. Guo, and J. Wen, “Pre-training of Context-aware Item Representation for Next Basket Recommendation,” Apr. 2019, doi: 10.48550/arXiv.1904.12604.
[34] M. Li, M. Ariannezhad, A. Yates, and M. de Rijke, “Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping,” in Proceedings of the 17th ACM Conference on Recommender Systems, in RecSys ’23. New York, NY, USA: Association for Computing Machinery, Sep. 2023, pp. 35–46. doi: 10.1145/3604915.3608803.
[35] W. Sun, R. Xie, J. Zhang, W. X. Zhao, L. Lin, and J.-R. Wen, “Generative Next-Basket Recommendation,” in Proceedings of the 17th ACM Conference on Recommender Systems, in RecSys ’23. New York, NY, USA: Association for Computing Machinery, Sep. 2023, pp. 737–743. doi: 10.1145/3604915.3608823.
[36] Z. Liu, X. Li, Z. Fan, S. Guo, K. Achan, and P. S. Yu, “Basket Recommendation with Multi-Intent Translation Graph Neural Network,” in 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA: IEEE, Dec. 2020, pp. 728–737. doi: 10.1109/BigData50022.2020.9377917.
[37] T. Liu and B. Liu, “Next basket recommendation based on graph attention network and transformer,” J. Phys.: Conf. Ser., vol. 2303, no. 1, p. 012023, Jul. 2022, doi: 10.1088/1742-6596/2303/1/012023.
[38] J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, no. null, pp. 281–305, Feb. 2012.
[39] J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2, in NIPS’12. Red Hook, NY, USA: Curran Associates Inc., Dec. 2012, pp. 2951–2959.
[40] R. N. and D. Ali, “Factors affecting consumer buying behavior,” Sep. 2016.
[41] A. J. Badgaiyan, A. Verma, and S. Dixit, “Impulsive buying tendency: Measuring important relationships with a new perspective and an indigenous scale,” IIMB Management Review, vol. 28, no. 4, pp. 186–199, Dec. 2016, doi: 10.1016/j.iimb.2016.08.009.
[42] W.-Y. Lee, “Association-Based Sequential Basket Recommendation.” Accessed: Mar. 13, 2024. [Online]. Available: https://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22111NCU05396072%22.&searchmode=basic
[43] Y. Goldberg and O. Levy, “word2vec Explained: deriving Mikolov et al.’s negative-sampling word-embedding method,” Feb. 2014, doi: 10.48550/arXiv.1402.3722.
[44] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Jan. 1997, doi: 10.1162/neco.1997.9.8.1735.
[45] H. Ying, F. Zhuang, F. Zhang, Y. Liu, G. Xu, X. Xie, H. Xiong, and J. Wu, “Sequential recommender system based on hierarchical attention network,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, in IJCAI’18. Stockholm, Sweden: AAAI Press, Jul. 2018, pp. 3926–3932.

指導教授

陳彥良(Yen-Liang Chen)

審核日期

2024-7-9

推文