博碩士論文 106423046 詳細資訊




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:16 、訪客IP:18.210.23.15
姓名 楊鈞元(Chun-Yuan Yang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱
(A Novel NMF-Based Movie Recommendation with Time Decay)
相關論文
★ Opinion Leader Discovery in Dynamic Social Networks★ 深度學習模型於工業4.0之機台虛擬量測應用
★ 以類別為基礎sequence-to-sequence模型之POI旅遊行程推薦★ A DQN-Based Reinforcement Learning Model for Neural Network Architecture Search
★ 遞迴類神經網路結合先期工業廢水指標之股價預測研究
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 (2022-7-31以後開放)
摘要(中) 現今最流行處理大量數據集的方法之一是矩陣分解(MF)技術,矩陣分解常用於在推薦系統中,因為其預測用戶興趣有著非常高的準確度。特別是非負值矩陣分解(NMF)已經被證明能夠非常有效利用多變量數據集的分解。然而即使是NMF技術,也無法完全捕捉到時間對於用戶喜好的影響程度。
本研究利用使用者因為時間影響而對喜好的改變,我們提出兩個基於傳統NMF的創新推薦系統 Dec_NMF,透過有效的時間影響,考慮使用者對於喜好的改變。Dec_NMF 包含了人類喜好行為隨著時間改變的概念,考慮使用者目前喜好的偏好,並將評分時間過長的資訊做衰減的處理。
本研究立用了三種線性以及三種非線性函數來調整評分,設定不同的潛在因素數量,透過均方誤差、均方根誤差、平均絕對誤差指標來評估模型好壞,並使用準確度、精密度、召回率與F1方法衡量模型的效能。實驗結果表明提出的方法中,在大部分的狀況優於傳統非負值矩陣分解。此外我們將所提出的模型應用在MovieLens資料集上,來演示所提出模型的有效性。
摘要(英) One of the most popular approaches to handle very large datasets is matrix factorization(MF) technique. The MF method was commonly used in recommendation systems due to the precise prediction of the user’s interest. Especially one of the successful method Non-Negative Matrix Factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. NMF-based techniques, however, could not properly capture time influences on user preferences.
In this paper, by considering time impacts on preferences, we propose two novel NMF-based recommendation system, Dec_NMF, to consider user preferences over time. Our proposed method extends the concept of the change of human interest through time to capture user’s current preference and reduce impacts which was rated from a long time ago. We adjust the rating using three different linear and three different non-linear time decay. Each function represents different decay degree of preferences to simulate the human’s interest behavior. The experimental results show that proposed methods outperforms the traditional MF and NMF model. Furthermore, we apply Dec_NMF on MovieLens datasets to demonstrate the effectiveness of Dec_NMF recommendation.
關鍵字(中) ★ 推薦系統
★ 隱因子模型
★ 矩陣分解
★ 線性代數演算法
★ 非線性代數演算法
關鍵字(英) ★ recommendation system
★ latent factor model
★ matrix factorization
★ linear algebra algorithm
★ non-linear algebra algorithm
論文目次 中文摘要 i
Abstract ii
Table of contents iii
1. Introduction 1
2. Related Work and Preliminary 9
2.1 Matrix Factorization 9
2.2 Recommendation on MF 12
2.3 Time Influence 14
2.4 Preliminary 15
3. Proposed Recommendation System: Dec_NMF 16
3.1 Time decay 16
3.1.1 Linear time decay 18
3.1.2 Non-linear time decay 21
3.2 Non-negative Matrix Factorization 25
3.3 Prediction Module of Dec_NMF 27
4. Performance Evaluation 28
4.1 Experiment setup 28
4.2 Effectiveness Evaluation 31
4.3 Analysis on Overall Performance 35
4.4 Comparing Model Performance on Precision, Recall and F1 score 36
4.5 Discussion on Parameter Settings 46
5. Conclusion 47
Reference 48
參考文獻 [1] Barão, S. M. M. (2008). Linear and Non-linear time series analysis: forecasting financial markets (Doctoral dissertation).
[2] Bursac, Z., Gauss, C. H., Williams, D. K., & Hosmer, D. W. (2008). Purposeful selection of variables in logistic regression. Source code for biology and medicine, 3(1), 17.
[3] C. Wang, Q. Liu, R. Wu, E. Chen, C. Liu, X. Huang and Z. Huang, “Confidence-Aware Matrix Factorization for Recommender Systems,” 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 434-442, 2018.
[4] Cai, D., He, X., Wu, X., & Han, J. (2008, December). Non-negative matrix factorization on manifold. In 2008 Eighth IEEE International Conference on Data Mining (pp. 63-72). IEEE.
[5] Cheng, Y., Yin, L., & Yu, Y. (2014, December). LorSLIM: low rank sparse linear methods for top-n recommendations. In 2014 IEEE International Conference on Data Mining (pp. 90-99). IEEE.
[6] Donner, R. V., Zou, Y., Donges, J. F., Marwan, N., & Kurths, J. (2010). Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics, 12(3), 033025.
[7] F. CHUA, R. Oentaryo and E. LIM, “Modeling Temporal Adoptions Using Dynamic Matrix Factorization,” IEEE 13th International Conference on Data Mining (ICDM), pp. 91-100, 2013.
[8] G. Trigeorgis, K. Bousmalis, S. Zafeiriou and B. Schuller, “A Deep Matrix Factorization Method for Learning Attribute Representations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, issue: 3, pp. 417-429, 2017.
[9] G. Zeng, H. Zhu, Q. Liu, P. Luo, E. Chen and T. Zhang, “Matrix Factorization with Scale-Invariant Parameters,” 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 4017-4024, 2015.
[10] H. Ma, H. Yang, M. Lyu and I. King, “SoRec: social recommendation using probabilistic matrix factorization,” 17th ACM conference on Information and knowledge management (CIKM), pp. 931-940, 2008.
[11] H. Park, J. Jung and U. Kang, “A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems,” IEEE International Conference on Big Data (IEEE BigData), pp. 756-765, 2017.
[12] H. Yu, H. Huang, I. Dihillon and C. Lin, “A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information,” 31st AAAI Conference on Artificial Intelligence (AAAI), pp. 2845-2851, 2017.
[13] He, X., Zhang, H., Kan, M. Y., & Chua, T. S. (2016, July). Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 549-558). ACM
[14] Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative Filtering for Implicit Feedback Datasets. In ICDM (Vol. 8, pp. 263-272).
[15] J. He, X. Li, L. Liao, D. Song, and K. Cheung, “Inferring a personalized next point-of-interest recommendation model with latent behavior patterns,” Proceedings of the 13th AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 137-143, 2016.
[16] J. Kawale, H. Bui, B. Kveton, L. Thanh and S. Chawla, “Efficient Thompson Sampling for Online Matrix-Factorization Recommendation,” 29th Conference on Neural Information Processing Systems (NIPS), 2015.
[17] J. Tu, G. Yu, C. Domeniconi, J. Wang, G. Xiao and M. Guo, “Multi-Label Answer Aggregation based on Joint Matrix Factorization,” IEEE 18th International Conference on Data Mining (ICDM), pp. 517-526, 2018.
[18] J. Yoo and S. Choi, “Probabilistic matrix tri-factorization,” Proceeding of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2009), pp. 1553-1556, 2009.
[19] J. Zhang and C. Chow, “CRATS: An LDA-Based Model for Jointly Mining Latent Communities, Regions, Activities, Topics, and Sentiments from Geosocial Network Data,” IEEE Transactions on Knowledge and Data Engineering (TKDE 2016), vol. 28, no. 11, pp. 2895–2909, 2016.
[20] Kabbur, S., & Karypis, G. (2014, December). Nlmf: Nonlinear matrix factorization methods for top-n recommender systems. In 2014 IEEE International Conference on Data Mining Workshop(pp. 167-174). IEEE.
[21] Kantz, H., & Schreiber, T. (2004). Nonlinear time series analysis(Vol. 7). Cambridge university press.
[22] Koren, Y. (2009, June). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 447-456). ACM.
[23] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, (8), 30-37.
[24] Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788.
[25] Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in neural information processing systems (pp. 556-562).
[26] M. Abdi, G. Okeyo and R. Mwangi, “Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey,” Computer and Information Science, vol. 11, no. 2, 2018.
[27] M. Jamali and M. Ester, “A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks,” 4th ACM Conference on Recommender Systems (RecSys), pp. 135-142, 2010.
[28] Ma, H., Yang, H., Lyu, M. R., & King, I. (2008, October). Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 931-940). ACM.
[29] Mavridis, A., & Μαυρίδης, Α. (2017). Matrix factorization techniques for recommender systems.
[30] N. Nghe, L. Drumond, T. Horváth, A. Nanopoulos, and L. Thieme, “Matrix and Tensor Factorization for predicting Student Performance,” 3rd International Conference on Computer Supported Education (CSEDU), 2011.
[31] N. Sorkunlu, D. Luong and V. Chandola, “dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems,” IEEE International Conference on Big Data (IEEE BigData), 2018.
[32] Ning, X., & Karypis, G. (2011, December). Slim: Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th International Conference on Data Mining (pp. 497-506). IEEE.
[33] Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial intelligence review, 13(5-6), 393-408.
[34] Q. Meng, H. Zhu, K. Xiao and H. Xiong, “Intelligent Salary Benchmarking for Talent Recruitment: A Holistic Matrix Factorization Approach,” IEEE 18th International Conference on Data Mining (ICDM), pp. 337-346, 2018.
[35] Q. Wang, P. Tan and J. Zhou, “Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization,” IEEE 18th International Conference on Data Mining (ICDM), pp. 1284-1289, 2018.
[36] Q. Wu and C. Pu, “Modeling and implementing collaborative editing systems with transactional techniques,” Proceedings of the 6th International ICST Conference on Collaborative Computing: Networking, Applications, Worksharing (CollaborateCom 2010), pp. 1-10, 2010.
[37] R. Mehta and K. Rana, “A review on matrix factorization techniques in recommender systems,” The 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA 2017), pp. 269-274, 2017.
[38] R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” ACM 20th International Conference on Neural Information Processing Systems (NIPS), pp. 1257-1264, 2007.
[39] S. Huang, M. Xu, M. Xie, M. Sugiyama, G. Niu and S. Chen, “Active Feature Acquisition with Supervised Matrix Completion,” 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 1571-1579, 2018.
[40] S. Zhao, M. Lyu, and I. King, “STELLAR: Spatial-Temporal Latent Ranking Model for Successive POI Recommendation,” Springer Briefs in Computer Science Point-of-Interest Recommendation in Location-Based Social Networks, pp. 79–94, 2018.
[41] Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Www, 1, 285-295.
[42] Strömqvist, Z. (2018). Matrix factorization in recommender systems: How sensitive are matrix factorization models to sparsity?.
[43] Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009.
[44] T. Wallace, C. Godwin, J. Thomson, and A. Tjernlund “The Definitive Guide to Selling on Amazon,” BigCommerce, pp. 1-229, 2019.
[45] V. Yuvaraj and N. SivaKumar, “A Semi- Non-Negative Matrix Factorization and Principal Component Analysis Unified Framework for Data Clustering,” International Journal of Advanced Research in Science, Engineering and Technology (IJARSET), vol. 5, issue 1, 2018.
[46] Vyas, P., Vasam, A., Damera, A., Thatipelli, H., Phatak, D. B., Karmali, N., & Saha, U. (2018). Recommender System for Collaborative Communities.
[47] W. Ma, Y. Wu, M. Gong, C. Qin and S. Wang, “Local Probabilistic Matrix Factorization for Personal Recommendation,” 13th International Conference on Computational Intelligence and Security (CIS), pp. 97-101, 2017.
[48] X. Luo, M. Zhou, Y. Xia and Q. Zhu, “An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems,” IEEE Transactions on Industrial Informatics, vol. 10, issue: 2, pp. 1273-1284, 2014.
[49] Xia, C., Jiang, X., Liu, S., Luo, Z., & Yu, Z. (2010, August). Dynamic item-based recommendation algorithm with time decay. In 2010 Sixth International Conference on Natural Computation(Vol. 1, pp. 242-247). IEEE.
[50] Y. Du, C. Xu and D. Tao, “Privileged Matrix Factorization for Collaborative Filtering,” 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 1610-1616, 2017.
[51] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” IEEE Computer, 42: 30-37, 2009.
[52] Z. Wu, H. Tian, X. Zhu, and S. Wang, “Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality,” Third International Conference on Data Mining and Big Data (DMBD), LNCS 10943: 114-125, 2018.
指導教授 陳以錚(Yi-Cheng Chen) 審核日期 2019-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明