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姓名	鍾昌桓(Chang-Huan Zhong)  查詢紙本館藏	畢業系所	資訊管理學系
論文名稱	(T3CRec: A Contextual Awareness Transformer-Based Recommendation System)
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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 (2028-7-1以後開放)
摘要(中)	隨著電子商務蓬勃興起，網路上的項目種類亦持續增加，使用者搜尋符合需求和偏好的項目時，經常需投入大量時間與精力。在這種情況下，推薦系統能藉由提供專屬且量身訂作的推薦，以實現商業目標，顯示出其不可或缺的重要性。因此，我們在本研究中介紹一種創新的推薦模型，名為 T3CRec，以解決現有方法常常無法有效捕捉到用戶與項目互動的更全面的上下文與特性。透過將項目類別、用戶特性及項目流行度等三種上下文因素融合至基於變壓器的架構中，T3CRec 能提供更具多樣性和個人化的推薦。我們將項目類別整合到項目的表徵中，並利用矩陣分解的方法，將用戶與項目的互動資訊映射到低維空間以進行資料增強。此外，我們引進了一種新穎的計算項目流行度的方式，考量用戶與項目之間的平均互動間隔的項目熱門度。通過在真實數據集上進行的廣泛實驗，我們證明了我們的模型在所有評價指標上優於現有的最先進的推薦模型，並平均提升 2.63% 推薦性能。
摘要(英)	As e-commerce thrives, the variety of online items continues to surge, with users often requiring users to invest significant time and energy in searching for items that meet their needs and preferences. In this scenario, the indispensable role of recommendation systems becomes evident, as they can deliver tailored recommendations to achieve business goals. Therefore, in this paper, we introduce an innovative recommendation model named T3CRec, designed to overcome the common inability of existing methods to effectively capture a more comprehensive context and characteristics of user-item interactions. By incorporating three contextual factors - item category, user characteristics, and item popularity - into a transformer-based framework, T3CRec can provide more diverse and personalized recommendations. We integrate item categories into item representations and use matrix factorization to map user-item interaction information into a low-dimensional space for data augmentation. Furthermore, we propose a novel way to compute item popularity, considering the average interaction interval between users and items. Through extensive experiments on real-world datasets, we demonstrated that our model outperforms the existing state-of-the-art recommendation models across all evaluation metrics, achieving an average improvement of 2.63% in recommendation performance.
關鍵字(中)	★ 推薦系統 ★ 變壓器 ★ 深度學習	關鍵字(英)	★ Recommendation System ★ Transformer ★ Deep Learning
論文目次	摘 要 i Abstract ii Table of Contents iii List of Figures iv List of Tables v 1. Introduction 1 2. Related Work 7 2.1 Traditional Recommendation 7 2.2 Sequential Recommendation 9 2.3 Popularity 11 3. Proposed Method 12 3.1 Categorical Context Attachment & Personality Context Augmentation 13 3.2 T3CRec Constraining 15 3.3 Popularity Context Customization & Prediction 18 4. Experiments and Evaluation 20 4.1 Evaluation Metrics and Baseline Models 21 4.2 Performance Comparison 23 4.3 Balance Weight Significance Discussion 26 4.4 Dimensionality Setting Analysis 28 4.5 Max Length Influence Discussion 30 4.6 Ablation Study 32 4.7 Case Study 33 5. Conclusion 35 Reference 36
參考文獻	[1] A. Thakur, "Trends and Analysis of E-Commerce Market: A Global Perspective," INTERNATIONAL JOURNAL OF APPLIED MARKETING, p. 11, 2021. [2] R. Sirvi, R. Ramu, V. Revelli, S. Kumar, and S. Pattnaik, "The role of E-commerce on customer engagement in 2021," International Journal of Scientific Development and Research, vol. 6, no. 2, pp. 319-324, 2021. [3] J. B. Schafer, J. Konstan, and J. Riedl, "Recommender systems in e-commerce," in Proceedings of the 1st ACM conference on Electronic commerce, 1999, pp. 158-166. [4] C. Basu, H. Hirsh, and W. Cohen, "Recommendation as classification: Using social and content-based information in recommendation," in Proceedings of the 15th National Conference on Artificial Intelligence, 1998, pp. 714-720. [5] M. J. Pazzani and D. Billsus, "Content-based recommendation systems," The adaptive web: methods and strategies of web personalization, pp. 325-341, 2007. [6] D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, E. Gómez, and P. Herrera, "Semantic audio content-based music recommendation and visualization based on user preference examples," Information Processing & Management, vol. 49, no. 1, pp. 13-33, 2013. [7] A. Van den Oord, S. Dieleman, and B. Schrauwen, "Deep content-based music recommendation," Advances in neural information processing systems, vol. 26, 2013. [8] 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, 2001, pp. 285-295. [9] K. Niemann and M. Wolpers, "A new collaborative filtering approach for increasing the aggregate diversity of recommender systems," in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 955-963. [10] J. Wang, A. P. De Vries, and M. J. Reinders, "Unifying user-based and item-based collaborative filtering approaches by similarity fusion," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006, pp. 501-508. [11] Z.-D. Zhao and M.-S. Shang, "User-based collaborative-filtering recommendation algorithms on hadoop," in 2010 third international conference on knowledge discovery and data mining, 2010: IEEE, pp. 478-481. [12] T. Hofmann, "Collaborative filtering via gaussian probabilistic latent semantic analysis," in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003, pp. 259-266. [13] S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, "Autorec: Autoencoders meet collaborative filtering," in Proceedings of the 24th international conference on World Wide Web, 2015, pp. 111-112. [14] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, "Neural collaborative filtering," in Proceedings of the 26th international conference on world wide web, 2017, pp. 173-182. [15] A. Popescul, L. H. Ungar, D. M. Pennock, and S. Lawrence, "Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments," arXiv preprint arXiv:1301.2303, 2013. [16] Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, vol. 42, no. 8, pp. 30-37, 2009. [17] M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks," in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 135-142. [18] P. Pirasteh, D. Hwang, and J. J. Jung, "Exploiting matrix factorization to asymmetric user similarities in recommendation systems," Knowledge-Based Systems, vol. 83, pp. 51-57, 2015. [19] J. Kawale, H. H. Bui, B. Kveton, L. Tran-Thanh, and S. Chawla, "Efficient Thompson Sampling for Online Matrix-Factorization Recommendation," Advances in neural information processing systems, vol. 28, 2015. [20] T. Tran, K. Lee, Y. Liao, and D. Lee, "Regularizing matrix factorization with user and item embeddings for recommendation," in Proceedings of the 27th ACM international conference on information and knowledge management, 2018, pp. 687-696. [21] H.-J. Xue, X. Dai, J. Zhang, S. Huang, and J. Chen, "Deep matrix factorization models for recommender systems," in IJCAI, 2017, vol. 17: Melbourne, Australia, pp. 3203-3209. [22] Y. Jiang, J. Xiong, and X. Lang, "Semi Nonnegative Matrix Factorization Recommendation Algorithm Based On Bayesian Probability Model," in 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020: IEEE, pp. 208-214. [23] R. Barathy and P. Chitra, "Applying matrix factorization in collaborative filtering recommender systems," in 2020 6th international conference on advanced computing and communication systems (ICACCS), 2020: IEEE, pp. 635-639. [24] T. Zeng, X. Fang, Y. Lang, J. Peng, X. Wu, S. Wang, and J. Gong, "Fair Personalized Recommendation through Improved Matrix Factorization by Neural Networks," in 2021 The 10th International Conference on Networks, Communication and Computing, 2021, pp. 19-24. [25] M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, "Recommender systems challenges and solutions survey," in 2019 international conference on innovative trends in computer engineering (ITCE), 2019: IEEE, pp. 149-155. [26] M. H. Abdi, G. Okeyo, and R. W. Mwangi, "Matrix factorization techniques for context-aware collaborative filtering recommender systems: A survey," 2018. [27] W. Juan, L. Yue-xin, and W. Chun-ying, "Survey of recommendation based on collaborative filtering," in Journal of Physics: Conference Series, 2019, vol. 1314, no. 1: IOP Publishing, p. 012078. [28] R. Mu, "A survey of recommender systems based on deep learning," IEEE Access, vol. 6, pp. 69009-69022, 2018. [29] 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), 2016: IEEE, pp. 191-200. [30] 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, 2016, pp. 729-732. [31] S. Wu, W. Ren, C. Yu, G. Chen, D. Zhang, and J. Zhu, "Personal recommendation using deep recurrent neural networks in NetEase," in 2016 IEEE 32nd international conference on data engineering (ICDE), 2016: IEEE, pp. 1218-1229. [32] T. Donkers, B. Loepp, and J. Ziegler, "Sequential user-based recurrent neural network recommendations," in Proceedings of the eleventh ACM conference on recommender systems, 2017, pp. 152-160. [33] X. Chen, H. Xu, Y. Zhang, J. Tang, Y. Cao, Z. Qin, and H. Zha, "Sequential recommendation with user memory networks," in Proceedings of the eleventh ACM international conference on web search and data mining, 2018, pp. 108-116. [34] B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, "Session-based recommendations with recurrent neural networks," Proceedings of the 4th International Conference on Learning Representations, 2016. [35] B. Hidasi and A. Karatzoglou, "Recurrent neural networks with top-k gains for session-based recommendations," in Proceedings of the 27th ACM international conference on information and knowledge management, 2018, pp. 843-852. [36] C.-Y. Wu, A. Ahmed, A. Beutel, A. J. Smola, and H. Jing, "Recurrent recommender networks," in Proceedings of the tenth ACM international conference on web search and data mining, 2017, pp. 495-503. [37] Y. Liu, Z. Ren, W.-N. Zhang, W. Che, T. Liu, and D. Yin, "Keywords generation improves e-commerce session-based recommendation," in Proceedings of The Web Conference 2020, 2020, pp. 1604-1614. [38] W. Ye, S. Wang, X. Chen, X. Wang, Z. Qin, and D. Yin, "Time matters: Sequential recommendation with complex temporal information," in Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 2020, pp. 1459-1468. [39] J. Bao and Y. Zhang, "Time-Aware Recommender System via Continuous-Time Modeling," in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 2872-2876. [40] T. X. Tuan and T. M. Phuong, "3D convolutional networks for session-based recommendation with content features," in Proceedings of the eleventh ACM conference on recommender systems, 2017, pp. 138-146. [41] J. Tang and K. Wang, "Personalized top-n sequential recommendation via convolutional sequence embedding," in Proceedings of the eleventh ACM international conference on web search and data mining, 2018, pp. 565-573. [42] 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, 2019, pp. 582-590. [43] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017. [44] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. [45] W.-C. Kang and J. McAuley, "Self-attentive sequential recommendation," in 2018 IEEE international conference on data mining (ICDM), 2018: IEEE, pp. 197-206. [46] C. Zhou, J. Bai, J. Song, X. Liu, Z. Zhao, X. Chen, and J. Gao, "Atrank: An attention-based user behavior modeling framework for recommendation," in Proceedings of the AAAI conference on artificial intelligence, 2018, vol. 32, no. 1. [47] L. Wu, S. Li, C.-J. Hsieh, and J. Sharpnack, "SSE-PT: Sequential recommendation via personalized transformer," in Proceedings of the 14th ACM Conference on Recommender Systems, 2020, pp. 328-337. [48] 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, 2019, pp. 1441-1450. [49] 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, 2020, pp. 322-330. [50] Z. Liu, Z. Fan, Y. Wang, and P. S. Yu, "Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer," in Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval, 2021, pp. 1608-1612. [51] S. Wang, L. Hu, Y. Wang, L. Cao, Q. Z. Sheng, and M. Orgun, "Sequential recommender systems: challenges, progress and prospects," arXiv preprint arXiv:2001.04830, 2019. [52] M. Xu, F. Liu, and W. Xu, "A survey on sequential recommendation," in 2019 6th International Conference on Information Science and Control Engineering (ICISCE), 2019: IEEE, pp. 106-111. [53] G. Adomavicius and A. Tuzhilin, "Context-aware recommender systems," in Recommender systems handbook: Springer, 2010, pp. 217-253. [54] H. Fang, D. Zhang, Y. Shu, and G. Guo, "Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations," ACM Transactions on Information Systems (TOIS), vol. 39, no. 1, pp. 1-42, 2020. [55] T. Chen, J. Guo, and W. Wu, "Graph representation learning for popularity prediction problem: a survey," arXiv preprint arXiv:2203.07632, 2022. [56] N. Jonnalagedda, S. Gauch, K. Labille, and S. Alfarhood, "Incorporating popularity in a personalized news recommender system," PeerJ Computer Science, vol. 2, p. e63, 2016. [57] Y. Ji, A. Sun, J. Zhang, and C. Li, "A re-visit of the popularity baseline in recommender systems," in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1749-1752. [58] H. Abdollahpouri, M. Mansoury, R. Burke, B. Mobasher, and E. Malthouse, "User-centered evaluation of popularity bias in recommender systems," in Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 2021, pp. 119-129.
指導教授	陳以錚(Yi-Cheng Chen)	審核日期	2023-7-20
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