§ 瀏覽學位論文書目資料
系統識別號 U0002-2808202319181500
DOI 10.6846/tku202300613
論文名稱(中文) 新型時間感知增強推薦系統
論文名稱(英文) A Novel Time-Awareness Augmentation Recommendation System
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 資訊工程學系全英語碩士班
系所名稱(英文) Master's Program, Department of Computer Science and Information Engineering (English-taught program)
外國學位學校名稱
外國學位學院名稱
外國學位研究所名稱
學年度 111
學期 2
出版年 112
研究生(中文) 劉柔含
研究生(英文) Jou-Han Liu
學號 610780040
學位類別 碩士
語言別 英文
第二語言別
口試日期 2023-07-06
論文頁數 29頁
口試委員 指導教授 - 王英宏(inhon@mail.tku.edu.tw)
口試委員 - 惠霖
口試委員 - 陳以錚
關鍵字(中) 個性化推薦系統
可解釋性推薦系統
Transformer
關鍵字(英) Personalized Recommendation System
Explainable Recommendation System
Transformer
第三語言關鍵字
學科別分類
第三語言摘要
論文目次
Chinese Abstract	I
Abstract	II
Table of Contents	V
List of Figures	 VI
List of Tables	 VII
Chapter 1 Introduction	1
Chapter 2 Related Work	4
2.1	Traditional Recommendation…	4
2.2	 Sequential Recommendation…	5
2.3	 Explainable & Trustworthy Recommendation…	7
Chapter 3 Proposed Model: ET- Transformer	11
3.1	Matrix Factorization	11
3.2	Transformer	12
3.3	Time Explainable	14
Chapter 4 Experiments	15
4.1	Baseline and Metrics	15
4.2	Performance and Discussion	17
4.3	Recall, Precision and F_Score Experiments Results	18
4.4	Ablation Study	19
4.5	Parameter Setting	21
4.6	Case Study	23
Chapter 5 Conclusion	24
Reference	25
 
List of Figures
Figure 1: The structure of ET-Transformer	11
Figure 2: Learning rate for different datasets	21
Figure 3: Batch-size for different datasets	22
Figure 4: Epochs for different datasets	23






 
List of Tables
Table 1: Statistics of MovieLens 100k & 1M, Books, Friday	15
Table 2: NDCG results for different models	17
Table 3: MRR results for different models	18
Table 4: Recall results for different models	18
Table 5: Precision results for different models	19
Table 6: F_Score results for different models	19
Table 7: Model comparison for NDCG in ablation study	19
Table 8: Model comparison for MRR in ablation study	20
Table 9: Model comparison for Recall in ablation study	20
Table 10: Model comparison for Precision in ablation study	20
Table 11: Model comparison for F_Score in ablation study	20
參考文獻
[1]	A. Carzaniga and C. P. Hall, “Content-Based Communication : a Research Agenda,” In Proceedings of the 6th international workshop on Software engineering and middleware, pp. 2-8, 2006
[2]	C. Wartena, W. Slakhorst and M. Wibbels “Selecting Keywords for Content Based Recommendation,” In Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 1533-1536, 2010
[3]	N. Bogaards and F. Schut “Content-based book recommendations Personalised and explainable recommendations without the cold-start problem,” In Proceedings of the 15th ACM Conference on Recommender Systems, pp. 545-547, 2021
[4]	G. Semeraro, P. Lops, P. Basile and M. D. Gemmis, “Knowledge Infusion into Content-based Recommender System,” In Proceedings of the third ACM conference on Recommender systems, pp. 301-304, 2009
[5]	A. Margara and G. Cugola “High performance content-based matching using GPUs,” In Proceedings of the 5th ACM international conference on Distributed event-based system, pp.183-194, 2011
[6]	G. Lixia and W. Junyi “Research on Collaborative Filtering Recommendation Algorithm for Improving User Similarity Calculation,” In Proceedings of the 2021 1st International Conference on Control and Intelligent Robotics, pp. 331–336, 2021
[7]	R. Zhang, Q. -d. Liu, Chun-Gui, J. -X. Wei and Huiyi-Ma, "Collaborative Filtering for Recommender Systems," 2014 Second International Conference on Advanced Cloud and Big Data, pp. 301-308, 2014
[8]	D. F. Murad, R. Hassan, B. D. Wijanarko, R. Leandros and S. A. Murad, "Evaluation of Hybrid Collaborative Filtering Approach with Context-Sensitive Recommendation System," 2022 7th International Conference on Business and Industrial Research (ICBIR), pp. 7-12, 2022
[9]	X. Li and F. Sun, "Sports training analysis method based on collaborative filtering," 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), Macau, China, pp. 83-87, 2021
[10]	X. Xu, "Matrix factorization recommendation algorithm based on deep neural network," 2019 2nd International Conference on Information Systems and Computer Aided Education, pp. 320-323, 2019 
[11]	K. Li, C.Li and L. Tian, “Matrix Factorization for Video Recommendation Based on Instantaneous User Interest.” In Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering. pp. 596–601, 2020
[12]	D. Le and H. Lauw, “Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches.” J. Artif. Int. Res. 70, pp. 1441–1479, 2021
[13]	K. Suekane et al., "Personalized Fashion Sequential Recommendation with Visual Feature Based on Conditional Hierarchical VAE," 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 362-365, 2022
[14]	Z. Zeng, X. Mu, X. Wei and T. Jiang, "Multi-Behavior Sequential Recommendation with Low-Rank Decomposed Self-Attention," 2022 2nd International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT), pp. 438-441, 2022
[15]	H. Huang and Y. Wang, "SRM: A Sequential Recommendation Model with Convolutional Neural Network and Multiple Features," 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE), pp. 49-52, 2021.
[16]	S. Jiang, X. Qian, T. Mei and Y. Fu, "Personalized Travel Sequence Recommendation on Multi-Source Big Social Media," in IEEE Transactions on Big Data, vol. 2, no. 1, pp. 43-56, 1 March 2016
[17]	H. Fang, D. Zhang, Y. Shu and G. Guo, " Deep Learning for Sequential Recommendation : Algorithms, Influential Factors, and Evaluations, " in ACM Transactions on Information Systems, Volume 39, Issue 1, Article 10, pp. 1-42, 2020
[18]	M. Zhang, S. Wu, X. Yu, Q. Liu and L. Wang, "Dynamic Graph Neural Networks for Sequential Recommendation," in IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 5, pp. 4741-4753, 1 May 2023
[19]	Z. Sun, B. Wu, Y. Chen and Y. Ye, "Learning From the Future: Light Cone Modeling for Sequential Recommendation," in IEEE Transactions on Cybernetics, vol. 53, no. 8, pp. 5358-5371, Aug. 2023
[20]	Zhuang. FZ, Zhou. YM, Ying. HC, Ao. X, Xie. X, He. Q, Xiong. H, "Sequential Recommendation via Cross-Domain Novelty Seeking Trait Mining," J. Comput. Sci. Technol. 35, pp. 305–319, 2020
[21]	H. Yu and M. O. Riedl, "Personalized Interactive Narratives via Sequential Recommendation of Plot Points," in IEEE Transactions on Computational Intelligence and AI in Games, vol. 6, no. 2, pp. 174-187, June 2014
[22]	B. Wu, X. He, L. Wu, X. Zhang and Y. Ye, "Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 7, pp. 4039-4051, July 2023
[23]	H. Li, X. Wang, Z. Zhang, J. Ma, P. Cui and W. Zhu, "Intention-Aware Sequential Recommendation With Structured Intent Transition," in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 11, pp. 5403-5414, 1 Nov. 2022
[24]	F. Zhang, "A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries," in IEEE Access, vol. 4, pp. 2714-2720, 2016
[25]	Lei. JS, Yang. S, Shi. W, Wu. Y, "Two-stage sequential recommendation for side information fusion and long-term and short-term preferences modeling, " J Intell Inf Syst 59, pp. 657–677, 2022
[26]	Y. Yang, H. -J. Jang and B. Kim, "A Hybrid Recommender System for Sequential Recommendation: Combining Similarity Models With Markov Chains," in IEEE Access, vol. 8, pp. 190136-190146, 2020
[27]	A. Alhejaili and S. Fatima, “Expressive Latent Feature Modelling for Explainable Matrix Factorisation-based Recommender Systems.” ACM Trans. Interact. Intell. Syst. 12, 3, Article 20 (September 2022), pp. 1-30, 2022
[28]	L. Li, Y. Zhang and L. Chen, “Personalized Transformer for Explainable Recommendation” In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 4947–4957, 2021
[29]	N. Maneechote and S. Maneeroj, "Explainable Recommendation via Personalized Features on Dynamic Preference Interactions," in IEEE Access, vol. 10, pp. 116326-116343, 2022
[30]	P. Bai, Y. Xia and Y. Xia, "Fusing Knowledge and Aspect Sentiment for Explainable Recommendation," in IEEE Access, vol. 8, pp. 137150-137160, 2020
[31]	Yuan-mei, W. & Shi-bo, W. Y, "Personalized Explainable Recommendation based on BERT, " Journal of Artificial Intelligence and Capsule Networks, 5(1), pp. 24-38, 2023
[32]	Y. Li, H. Chen, Y. Li, L. Li, P. S. Yu and G. Xu, "Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation," in IEEE Transactions on Knowledge and Data Engineering, 2023
[33]	Huang, Y, Zhao. F, Gui. X, Jin. H, "Path-enhanced explainable recommendation with knowledge graphs, " World Wide Web 24, pp. 1769–1789, 2021
[34]	L. Zhu, W. Wei, Y. Xia, and L. Fu, “Bus Travel Time Prediction Based on Multi-Source Data Fusion.” In Proceedings of the 5th International Conference on Big Data Technologies (ICBDT '22), pp. 97–102, 2022
[35]	A. Muñoz, D. Scarlatti, and P. Costas, “Real-time prediction of flight arrival times using surveillance information.” In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings (ECSA '18), pp. 1–4, 2018
[36]	R. Zhan, C. Pei, Q. Su, J. Wen, X. Wang, G. Mu, D. Zheng, P. Jiang, and K. Gai, “Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation.” In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), pp. 4472–4481, 2022
[37]	Chen. YC, Hui. L, & Thaipisutikul. T, "A collaborative filtering recommendation system with dynamic time decay, " J Supercomput 77, pp. 244–262, 2021
[38]	J. Chen, K. Li, Z. Tang, K. Bilal and K. Li, "A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment," in IEEE Access, vol. 4, pp. 1767-1783, 2016
[39]	Y. Wang, L. Han, "Adaptive time series prediction and recommendation, " Information Processing & Management, Volume 58, Issue 3, 2021
[40]	X. Wang, J. Zhu, Z. Zheng, W. Song, Y. Shen, and M. R. Lyu, "A Spatial-Temporal QoS Prediction Approach for Time-aware Web Service Recommendation, " ACM Trans. Web 10, 1, Article 7, 25 pages, 2016
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