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姓名	李宛毓(Wan-Yu Lee)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	基於關聯的購物籃序列推薦 (Association-Based Sequential Basket Recommendation)
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摘要(中)	下一個購物籃推薦旨在根據用戶過去的購物籃歷史序列資料，推薦用戶下一個購物籃的商品。然而，過去的購物籃推薦方法大多只是在學習用戶的購買歷史後，直接推薦預測的前 N 個商品，未考慮到商品之間的互補和替代關係。互補關係指的是兩個商品常常一起購買的關係，而替代關係則表示兩個商品之間可以相互替換。為了考慮商品的關聯性，過去的研究通常使用基於關聯規則的推薦系統，但這些方法僅考慮了商品之間的關聯，並未考慮到用戶購物籃之間的序列性關係和用戶的偏好。因此，本研究提出一種基於關聯的購物籃序列推薦模型ASBRec，使用基於序列的模型來構建購物籃推薦系統，以預測用戶在下一個購物籃中可能購買的商品。特別地，在模型的前端和後端加入了購物籃的關聯性考量。在前端，我們使用 Item2Vec 方法對商品進行編碼，將關聯性融入項目嵌入中。這樣做有助於捕捉商品之間的相似性和互補性。在後端，我們利用關聯規則建立信賴度矩陣，以獲得所有項目之間的信賴度。然後，我們將信賴度矩陣與輸出序列聚合，通過調整信賴度和輸出序列之間的權重，修正購物籃推薦的結果，並增強其關聯性。這種基於序列的購物籃推薦方法結合了商品關聯性和用戶偏好，能夠提供更精確和個性化的推薦結果。在本研究中，我們對一個真實世界的資料集進行了實驗，結果顯示我們提出的方法在推薦性能上優於先前的方法。
摘要(英)	The next basket recommendation aims to recommend items for the user′s next basket based on their past basket history. However, previous basket recommendation methods often simply recommend the top N predicted items directly after learning from the user′s purchase history, without considering the complementary and substitution relationships between items. Complementary relationship refers to the frequent co-purchasing of two items, while substitution relationship indicates that two items can be interchangeably chosen. To consider the interplay between items, previous research has typically relied on association rule-based recommendation systems. However, these methods only consider item associations and do not consider the sequential relationship between user baskets and user preferences. Therefore, this study proposes an Association-based Sequential Basket Recommendation model, ASBRec, which utilizes sequence-based models to construct a basket recommendation system for predicting the items that users are likely to purchase in the next basket. Notably, we incorporate considerations of basket associations in both the frontend and backend of the model. At the frontend, we encode items using the Item2Vec method, incorporating associations into item embeddings. This helps capture the similarity and complementarity between items. At the backend, we utilize association rules to establish a confidence matrix that captures the confidence between all items. We then aggregate the confidence matrix with the output sequence, adjusting the weights between confidence and the output sequence to refine the basket recommendation results and enhance their associations. This sequence-based basket recommendation approach combines item relationships and user preferences, providing more accurate and personalized recommendations. In this study, we conducted experiments on a real-world dataset, and the results demonstrate that our proposed method outperforms previous approaches in terms of recommendation performance.
關鍵字(中)	★ 基於關聯規則的方法 ★ 下一個購物籃推薦 ★ Item2Vec ★ 信賴度矩陣 ★ Transformer	關鍵字(英)	★ Association Rule-based ★ Next Basket Recommendation ★ Item2Vec ★ Confidence Matrix ★ Transformer
論文目次	摘要 i ABSTRACT ii List of Figures iv List of Tables v 1. Introduction 1 2. Related work 7 2-1 Traditional methods 7 2-1-1 Sequential methods 7 2-1-2 Collaborative Filtering Methods 7 2-1-3 Hybrid Methods 8 2-2 Neural Network methods 9 2-3 Association Rule-based 12 2-4 Summary 13 3. Proposed Research 14 3-1 Problem definition and symbol description 14 3-2 Overview 15 3-3 Preprocessing 17 3-3-1 Frontend association: Item Embedding 17 3-3-2 Backend Association: Confidence Matrix 21 3-4 Basket Preference Learning Module 22 3-4-1 Learning Basket Embeddings Using Self-attention 22 3-4-2 Learning Basket Sequences Using Transformer 23 3-5 Combination and prediction 27 3-6 Loss function 28 4. Experiment 28 4-1 Dataset 28 4-2 Baselines 30 4-3 Evaluation Metrics 31 4-4 Experimental Setting and Platform 33 5. Experimental results 34 5-1 Experiment 1: Comparison with Baseline 34 5-2 Experiment 2: Sensitivity Analysis 35 5-3 Experiment 3: Ablation Experiment 41 6. Summary 43 6-1 Conclusion 43 6-2 Future work 44 Reference 46
參考文獻	[1] A. Elbir and N. Aydin, “Music genre classification and music recommendation by using deep learning,” Electronics Letters, vol. 56, no. 12, pp. 627–629, 2020, doi: 10.1049/el.2019.4202. [2] M. He, B. Wang, and X. Du, “HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie Recommendation,” IEEE Access, vol. 7, pp. 30276–30284, 2019, doi: 10.1109/ACCESS.2019.2902398. [3] C. Wu, F. Wu, M. An, J. Huang, Y. Huang, and X. Xie, “NPA: Neural News Recommendation with Personalized Attention,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’19. New York, NY, USA: Association for Computing Machinery, Jul. 2019, pp. 2576–2584. doi: 10.1145/3292500.3330665. [4] F. Jouyandeh and P. M. Zadeh, “IPARS: An Image-based Personalized Advertisement Recommendation System on Social Networks,” Procedia Computer Science, vol. 201, pp. 375–382, Jan. 2022, doi: 10.1016/j.procs.2022.03.050. [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] S. Aftab and H. Ramampiaro, “Evaluating Top-N Recommendations Using Ranked Error Approach: An Empirical Analysis,” IEEE Access, vol. 10, pp. 30832–30845, 2022, doi: 10.1109/ACCESS.2022.3159646. [7] K. Wahyudi, J. Latupapua, R. Chandra, and A. S. Girsang, “Hotel Content-Based Recommendation System,” J. Phys.: Conf. Ser., vol. 1485, no. 1, p. 012017, Mar. 2020, doi: 10.1088/1742-6596/1485/1/012017. [8] H. Zarzour, Z. Al-Sharif, M. Al-Ayyoub, and Y. Jararweh, “A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques,” in 2018 9th International Conference on Information and Communication Systems (ICICS), Apr. 2018, pp. 102–106. doi: 10.1109/IACS.2018.8355449. [9] T. K. Paradarami, N. D. Bastian, and J. L. Wightman, “A hybrid recommender system using artificial neural networks,” Expert Systems with Applications, vol. 83, pp. 300–313, Oct. 2017, doi: 10.1016/j.eswa.2017.04.046. [10] A. S. Tewari, J. P. Singh, and A. G. Barman, “Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance,” Procedia Computer Science, vol. 132, pp. 1678–1684, Jan. 2018, doi: 10.1016/j.procs.2018.05.139. [11] M. Srilakshmi, G. Chowdhury, and S. Sarkar, “Two-stage system using item features for next-item recommendation,” Intelligent Systems with Applications, vol. 14, p. 200070, May 2022, doi: 10.1016/j.iswa.2022.200070. [12] M. Li, X. Bao, L. Chang, and T. Gu, “Modeling personalized representation for within-basket recommendation based on deep learning,” Expert Systems with Applications, vol. 192, p. 116383, Apr. 2022, doi: 10.1016/j.eswa.2021.116383. [13] M. Li, S. Jullien, M. Ariannezhad, and M. de Rijke, “A Next Basket Recommendation Reality Check,” ACM Trans. Inf. Syst., vol. 41, no. 4, p. 116:1-116:29, Apr. 2023, doi: 10.1145/3587153. [14] 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. [15] 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. [16] 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. [17] W. P. Nurmayanti, H. M. Sastriana, A. Rahim, M. Gazali, R. H. Hirzi, Z. Ramdani, and M. Malthuf, “Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data,” International Journal of Educational Research & Social Sciences, vol. 2, no. 1, Art. no. 1, Apr. 2021, doi: 10.51601/ijersc.v2i1.45. [18] S. S. Baby and S. L. Reddy, “End to End Product Recommendation system with improvements in Apriori Algorithm,” in 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Sep. 2021, pp. 1357–1361. doi: 10.1109/ICIRCA51532.2021.9544981. [19] A. G. Nabila, I. Nurma Yulita, I. Suryana, and M. Suryani, “Market Basket Analysis on Sales Transactions for Micro, Small and Medium Enterprises Using Apriori Algorithm to Support Business Promotion Strategy in RDA Hijab,” in 2021 International Conference on Artificial Intelligence and Big Data Analytics, Oct. 2021, pp. 1–6. doi: 10.1109/ICAIBDA53487.2021.9689770. [20] 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), Sep. 2016, pp. 1–6. doi: 10.1109/MLSP.2016.7738886. [21] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space.” arXiv, Sep. 06, 2013. doi: 10.48550/arXiv.1301.3781. [22] 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. [23] G. Shani, D. Heckerman, and R. I. Brafman, “An MDP-Based Recommender System,” Journal of Machine Learning Research, vol. 6, no. 43, pp. 1265–1295, 2005. [24] 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. [25] D. Bokde, S. Girase, and D. Mukhopadhyay, “Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey,” Procedia Computer Science, vol. 49, pp. 136–146, Jan. 2015, doi: 10.1016/j.procs.2015.04.237. [26] B. Loni, Y. Shi, M. Larson, and A. Hanjalic, “Cross-Domain Collaborative Filtering with Factorization Machines,” in Advances in Information Retrieval, M. de Rijke, T. Kenter, A. P. de Vries, C. Zhai, F. de Jong, K. Radinsky, and K. Hofmann, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2014, pp. 656–661. doi: 10.1007/978-3-319-06028-6_72. [27] J. Xiao, H. Ye, X. He, H. Zhang, F. Wu, and T.-S. Chua, “Attentional factorization machines: learning the weight of feature interactions via attention networks,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence, in IJCAI’17. Melbourne, Australia: AAAI Press, Aug. 2017, pp. 3119–3125. [28] 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 2021 13th International Conference on Machine Learning and Computing, in ICMLC 2021. New York, NY, USA: Association for Computing Machinery, Feb. 2021, pp. 477–484. doi: 10.1145/3457682.3457755. [29] 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. [30] B. Liu, H. Zhang, L. Kong, and D. Niu, “Factorizing Historical User Actions for Next-Day Purchase Prediction,” ACM Trans. Web, vol. 16, no. 1, p. 1:1-1:26, Sep. 2021, doi: 10.1145/3468227. [31] 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, in SIGIR ’18. New York, NY, USA: Association for Computing Machinery, Jun. 2018, pp. 1201–1204. doi: 10.1145/3209978.3210129. [32] W. Wang and L. Cao, “Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative Feedback,” ACM Trans. Inf. Syst., vol. 39, no. 3, p. 24:1-24:26, Feb. 2021, doi: 10.1145/3444368. [33] Y. Chen, J. Li, C. Liu, C. Li, M. Anderle, J. McAuley, and C. Xiong, “Modeling Dynamic Attributes for Next Basket Recommendation.” arXiv, Sep. 23, 2021. doi: 10.48550/arXiv.2109.11654. [34] 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. [35] D.-T. Le, H. W. Lauw, and Y. Fang, “Correlation-sensitive next-basket recommendation,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, in IJCAI’19. Macao, China: AAAI Press, Aug. 2019, pp. 2808–2814. [36] Q.-V. P. Hoang and D.-T. Le, “Modeling Multi-Intent Basket Sequences for Next-Basket Recommendation,” in 2021 13th International Conference on Knowledge and Systems Engineering (KSE), Jan. 2021, pp. 1–6. doi: 10.1109/KSE53942.2021.9648773. [37] Z. Liu, X. Li, Z. Fan, S. Guo, K. Achan, and P. S. Yu, “Basket Recommendation with Multi-Intent Translation Graph Neural Network.” arXiv, Oct. 21, 2020. doi: 10.48550/arXiv.2010.11419. [38] S. Zhao, W. Wei, D. Zou, and X. Mao, “Multi-View Intent Disentangle Graph Networks for Bundle Recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, Art. no. 4, Jun. 2022, doi: 10.1609/aaai.v36i4.20359. [39] Z. Shao, S. Wang, Q. Zhang, W. Lu, Z. Li, and X. Peng, “A Systematical Evaluation for Next-Basket Recommendation Algorithms.” arXiv, Sep. 06, 2022. doi: 10.48550/arXiv.2209.02892. [40] J. Chang, C. Gao, X. He, D. Jin, and Y. Li, “Bundle Recommendation and Generation with Graph Neural Networks,” IEEE Transactions on Knowledge and Data Engineering, pp. 1–1, 2021, doi: 10.1109/TKDE.2021.3114586. [41] Y. Zhang, B. Guo, Q. Wang, Y. Sun, and Z. Yu, “MGCN4REC: Multi-graph Convolutional Network for Next Basket Recommendation with Instant Interest,” in Green, Pervasive, and Cloud Computing, Z. Yu, C. Becker, and G. Xing, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020, pp. 171–185. doi: 10.1007/978-3-030-64243-3_14. [42] Q. Zhao and S. S. Bhowmick, “Association rule mining: A survey,” Nanyang Technological University, Singapore, vol. 135, 2003. [43] R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” SIGMOD Rec., vol. 22, no. 2, pp. 207–216, Jun. 1993, doi: 10.1145/170036.170072. [44] J. Han, J. Pei, Y. Yin, and R. Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach,” Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 53–87, Jan. 2004, doi: 10.1023/B:DAMI.0000005258.31418.83. [45] F. Wang, Y. Wen, J. Chen, and B. Cao, “Integrating Collaborative Filtering and Association Rule Mining for Market Basket Recommendation,” in Web Information Systems Engineering – WISE 2018, H. Hacid, W. Cellary, H. Wang, H.-Y. Paik, and R. Zhou, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018, pp. 19–34. doi: 10.1007/978-3-030-02925-8_2. [46] F. Wang, Y. Wen, T. Guo, J. Liu, and B. Cao, “Collaborative filtering and association rule mining-based market basket recommendation on spark,” Concurrency and Computation: Practice and Experience, vol. 32, no. 7, p. e5565, 2020, doi: 10.1002/cpe.5565. [47] X. Rong, “word2vec Parameter Learning Explained.” arXiv, Jun. 05, 2016. doi: 10.48550/arXiv.1411.2738. [48] 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. [49] 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. [50] 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.
指導教授	陳彥良(Yen-Liang Chen)	審核日期	2023-7-18
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