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| Title: | 推薦系統資料插補改良法-電影推薦系統應用
Improving recommendations through data imputation-with application for movie recommendation |
| Authors: | 楊智博
Yang, Chih Po |
| Contributors: | 翁久幸
Weng, Chiu Hsing
楊智博
Yang, Chih Po |
| Keywords: | 推薦系統
矩陣分解
隨機梯度下降
奇異值分解
Recommender systems
Matrix Factorization
Stochastic Gradient Descent
Alternating Least Squares
Singular Value Decomposition |
| Date: | 2015 |
| Issue Date: | 2015-10-01 14:12:21 (UTC+8) |
| Abstract: | 現今許多網路商店或電子商務將產品銷售給消費者的過程中，皆使用推薦系統的幫助來提高銷售量。如亞馬遜公司(Amazon)、Netflix，深入了解顧客的使用習慣，建構專屬的推薦系統並進行個性化的推薦商品給每一位顧客。
推薦系統應用的技術分為協同過濾和內容過濾兩大類，本研究旨在探討協同過濾推薦系統中潛在因子模型方法，利用矩陣分解法找出評分矩陣。在Koren等人(2009)中，將矩陣分解法的演算法大致分為兩種，隨機梯度下降法(Stochastic gradient descent)與交替最小平方法(Alternating least squares)。本研究主要研究目的有三項，一為比較交替最小平方法與隨機梯度下降法的預測能力，二為兩種矩陣分解演算法在加入偏誤項後的表現，三為先完成交替最小平方法與隨機梯度下降法，以其預測值對原始資料之遺失值進行資料插補，再利用奇異值分解法對完整資料做矩陣分解，觀察其前後方法的差異。
研究結果顯示，隨機梯度下降法所需的運算時間比交替最小平方法所需的運算時間少。另外，完成兩種矩陣分解演算法後，將預測值插補遺失值，進行奇異值分解的結果也顯示預測能力有提升。
Recommender system has been largely used by Internet companies such Amazon and Netflix to make recommendations for Internet users. Techniques for recommender systems can be divided into content filtering approach and collaborative filtering approach. Matrix factorization is a popular method for collaborative filtering approach. It minimizes the object function through stochastic gradient descent and alternating least squares.
This thesis has three goals. First, we compare the alternating least squares method and stochastic gradient descent method. Secondly, we compare the performance of matrix factorization method with and without the bias term. Thirdly, we combine singular value decomposition and matrix factorization.
As expected, we found the stochastic gradient descent takes less time than the alternating least squares method, and the the matrix factorization method with bias term gives more accurate prediction. We also found that combining singular value decomposition with matrix factorization can improve the predictive accuracy. |
| Reference: | 1. Resnick, Paul, et al. "GroupLens: an open architecture for collaborative filtering of netnews." Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM,1994.
2. Konstan, Joseph A., et al. "GroupLens: applying collaborative filtering to Usenet news." Communications of the ACM 40.3,1997,pp. 77-87.
3. Sarwar, Badrul, et al. "Item-based collaborative filtering recommendation algorithms." Proceedings of the 10th international conference on World Wide Web. ACM, 2001.
4. Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." Internet Computing, IEEE 7.1,2003,pp. 76-80.
5. Paterek, Arkadiusz. "Improving regularized singular value decomposition for collaborative filtering." Proceedings of KDD cup and workshop. Vol. 2007. 2007.
6. Takács, Gábor, et al. "Major components of the gravity recommendation system." ACM SIGKDD Explorations Newsletter 9.2,2007,pp. 80-83.
7. Takacs, Gabor, et al. "On the gravity recommendation system." Proceedings of KDD cup and workshop. Vol. 2007. 2007.
8. Ma, Chih-Chao. "A Guide to Singular Value Decomposition for Collaborative Filtering.",2008.
9. Koren, Yehuda. "The bellkor solution to the netflix grand prize." Netflix prize documentation 81,2009.
10. Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8,2009,pp. 30-37.
11. Gong, Songjie. "A collaborative filtering recommendation algorithm based on user clustering and item clustering." Journal of Software 5.7,2010,pp. 745-752.
12. Koren, Yehuda. "Collaborative filtering with temporal dynamics."Communications of the ACM 53.4,2010,pp. 89-97.
13. Bottou, Léon. "Large-scale machine learning with stochastic gradient descent."Proceedings of COMPSTAT`2010. Physica-Verlag HD, 2010,pp. 177-186.
14. 吳金龍,Netflix Prize 中的協同過濾算法,北京大學數學科學學院博士論文,2010
15. Ekstrand, Michael D., John T. Riedl, and Joseph A. Konstan. "Collaborative filtering recommender systems." Foundations and Trends in Human-Computer Interaction 4.2,2011,pp. 81-173.
16. 張孫浩,網路評比資料之統計分析,國立政治大學統計學系碩士論文,2011
17. Bottou, Léon. "Stochastic gradient descent tricks." Neural Networks: Tricks of the Trade. Springer Berlin Heidelberg, 2012,pp. 421-436.
18. Zhuang, Yong, et al. "A fast parallel sgd for matrix factorization in shared memory systems." Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013.
19. 張良卉,矩陣分解法對網路評比資料分析之探討,國立政治大學統計學系碩士論文,2013
20. Fang, Xiaowen. A Study of Recommender Systems with Applications. Diss. UNIVERSITY OF MINNESOTA,2014.
21. GroupLens Research. Retrieved JUN,2015,from http://www.grouplens.org |
| Description: | 碩士
國立政治大學
統計研究所
102354020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0102354020 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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