政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/78727
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113303/144284 (79%)
造访人次 : 50815678      在线人数 : 680
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/78727


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/78727


    题名: 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
    作者: 楊智博
    Yang, Chih Po
    贡献者: 翁久幸
    Weng, Chiu Hsing
    楊智博
    Yang, Chih Po
    关键词: 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
    日期: 2015
    上传时间: 2015-10-01 14:12:21 (UTC+8)
    摘要: 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.
    參考文獻: 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
    描述: 碩士
    國立政治大學
    統計研究所
    102354020
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0102354020
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    402001.pdf1019KbAdobe PDF2334检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈