政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111590

政大典藏 > College of Commerce > MBA Program > Theses > Item 140.119/111590

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111590

Title:	基於內隱資料之協同過濾推薦系統研究與實作 Research and application for collaborative filtering recommendation system using implicit datasets
Authors:	張遠耀 Chang, Yuan Yao
Contributors:	洪叔民 Horng, Shwu Min 張遠耀 Chang, Yuan Yao
Keywords:	推薦系統內隱資料協同過濾潛在因子矩陣分解
Date:	2017
Issue Date:	2017-07-31 11:35:33 (UTC+8)
Abstract:	近年來電子商務蓬勃發展，嚴重侵蝕實體通路業績，因此線下服務提供者更應善用資料科學技術，找出顧客未被滿足之需求，進而提供優質服務，其中脫穎而出的關鍵非推薦系統莫屬。本研究以運用計算產品相似程度的「項目導向協同過濾」和計算使用者與商品蘊含特徵的「潛在因子」兩大類「協同過濾」推薦方法為核心，藉由實體零售通路累積的顧客消費紀錄，驗證「協同過濾」方法較傳統熱門商品推薦機制更符合消費者偏好，且「協同過濾」方法能達到完全個人化推薦之目標。本研究使用的實體零售通路消費紀錄源於顧客真實購物行為，收集成本低，且數據量龐大，然而此類資料無法直接傳達顧客對於商品的喜好與滿足程度，因此被稱之為「內隱資料」，針對內隱資料處理上，本研究選擇以消費次數取代金額，提出短期重複行為計算閾值概念，以時間修正權重處理可能的偏好轉變與習慣性消費。模型評估方面，透過強調推薦順序的「平均排名百分比」作為指標，利用傳統熱門商品推薦為基準，比較「項目導向協同過濾」和「潛在因子」兩大類「協同過濾」方法推薦品質的優劣，本研究顯示兩大類「協同過濾」方法達到的推薦品質皆優於熱門商品推薦，且前者遞交的推薦清單為完全個人化，運用本研究發展的推薦系統，將其導入與應用，讓線下服務提供者在與每位顧客接觸的關鍵時刻，能在洞悉對方需求的利基上，提供令顧客滿意的商品與服務，創造獨特且難以模仿的競爭優勢。
Reference:	[1] How retailers can keep up with consumers. Retrieved October 2013. from: http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers [2] Netflix Prize. from: http://www.netflixprize.com/community/forum.html [3] YouTube statistics. from: https://www.youtube.com/yt/press/statistics.html [4] Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., & Sampath, D. (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on Recommender systems. 293–296. [5] Facebook newsroom, company info, statistics. from: https://www.youtube.com/yt/press/zh-TW/statistics [6] Recommending items to more than a billion people. Retrieved June 3 2015. from: https://code.facebook.com/posts/861999383875667/recommending-items-to-morm-than-a-billion-people/ [7] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM. 35(12). 61-70. [8] Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM. 40(3). 77-87. [9] Harper, F. M., & Konstan, J. A. (2016). The MovieLens datasets: history and context. ACM Transactions on Interactive Intelligent Systems (TiiS). 5(4). Article No.: 19. 1-20. [10] Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet computing. 7(1). 76-80. [11] Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. 191-198. [12] Bell, R. M., & Koren, Y. (2007). Lessons from the Netflix prize challenge. ACM Sigkdd Explorations Newsletter. 9(2). 75-79. [13] Listen to Pandora, and it listens back. Retrieved January 4 2014. from: https://www.nytimes.com/2014/01/05/technology/pandora-mines-users-data-to-better-target-ads.html?_r=0 [14] Ali, K., & Van Stam, W. (2004). TiVo: making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 394-401. [15] Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review. 13(5-6). 393-408. [16] Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. The adaptive web. 325-341. [17] Ramos, J. (2003). Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning. [18] Tata, S., & Patel, J. M. (2007). Estimating the selectivity of TF-IDF based cosine similarity predicates. ACM Sigmod Record. 36(2). 7-12. [19] Huang, A. (2008). Similarity measures for text document clustering. In Proceedings of the sixth New Zealand computer science research student conference. 49-56. [20] Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS). 22(1). 5-53. [21] Papagelis, M., & Plexousakis, D. (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence. 18(7). 781-789. [22] Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering. 17(6). 734-749. [23] Wang, J., De Vries, A. P., & Reinders, M. J. (2006). 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. 501-508. [24] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. 285-295. [25] Yildirim, H., & Krishnamoorthy, M. S. (2008). A random walk method for alleviating the sparsity problem in collaborative filtering. In Proceedings of the 2008 ACM conference on Recommender systems. 131-138. [26] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer. 42(8). 30-37. [27] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Application of dimensionality reduction in recommender system-a case study. Minnesota Univ Minneapolis Dept of Computer Science. [28] Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup and workshop. Vol. 2007. 5-8. [29] Netflix Update: Try This at Home. Retrieved December 2006. from: http://sifter.org/~simon/journal/20061211.html [30] Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. 452-461. [31] Bennett, J., & Lanning, S. (2007). The Netflix Prize. In Proceedings of KDD cup and workshop. Vol. 2007. 3-6. [32] Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM`08. Eighth IEEE International Conference. 263-272. [33] Surprise (Python scikit for recommender systems), Similarities Module Introduction. from: http://surprise.readthedocs.io/en/latest/similarities.html [34] Koren, Y. (2010). Factor in the neighbors: scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data. 4(1). Article No.: 1. 1-24. [35] Surprise (Python scikit for recommender systems), Matrix Factorization-based algorithms. from: http://surprise.readthedocs.io/en/latest/matrix_factorization.html [36] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce. 158-167. [37] What do Recommender Systems experts think of the "Estimating the causal impact of recommendation systems from observational data" paper ? Answer by Xavier Amatriain. from: https://www.quora.com/What-do-Recommender- Systems-experts-think-of-the-Estimating-the-causal-impact-of-recommendation-systems-from-observational-data-paper [38] Baltrunas, L., & Amatriain, X. (2009). Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS’09). [39] Gomez-Uribe, C. A., & Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS). 6(4). Article No.: 13. 1-19.
Description:	碩士國立政治大學企業管理研究所(MBA學位學程) 104363050
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G1043630501
Data Type:	thesis
Appears in Collections:	[MBA Program] Theses

Files in This Item:

There are no files associated with this item.

社群 sharing

Loading...