English  |  正體中文  |  简体中文  |  Items with full text/Total items : 75002/106093 (71%)
Visitors : 19428501      Online Users : 604
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/101253


    Title: 以使用者意見提升推薦系統效能之研究
    Exploiting User Opinions for Improving Individual Recommendations
    Authors: 林金永
    Contributors: 蔡銘峰
    林金永
    Keywords: 推薦系統
    協同過濾
    文字探勘
    Recommender Systems
    Collaborative Filtering
    Text Mining
    Factorization Machines
    Date: 2016
    Issue Date: 2016-09-02 01:32:47 (UTC+8)
    Abstract: 近年來,受惠於網路的盛行及其帶來的便利性,許多網
    站得以收集到大量的使用者對於商品之評價以及評論,運用
    這些使用者的回饋資料進行分析,以更精準的進行商業行銷
    正是當今浪潮。
    而推薦系統廣泛應用於商業行銷,常用的推薦系統之計
    算理論,乃依據使用者對商品的評分進行協同式的過濾,以
    找出合適的產品給予推薦,其理論的基礎是品味相近的消費
    者應該會喜歡類似的商品,使用者對商品的評分即為此模式
    所採用的依據,例如:運用User-based Collaborative Filtering
    ,可以找出與被推薦者的特徵值類似的使用者,並以類似使
    用者中較高評分的項目作為推薦清單,這種方式能得到相當
    不錯的推薦結果,且計算的運算量亦不太大。
    相較之下,以使用者對商品的文字評論作為依據的推薦
    方法則較為少見,但我們認為文字訊息在推薦系統中亦佔有
    相當份量的重要性;直覺上,將使用者的評分與其文字評論
    作結合進行分析,應可更完整呈現該使用者的意向,並進而
    應能改進推薦系統之推薦效能。在這份論文研究中,我們嘗
    試結合使用者對商品的評分與文字評論於推薦系統中,並以
    一份取自TripAdvisor.com的使用者對於飯店評價之資料集進
    行實驗,透過libFM 建立推薦模型;從實驗結果探討中印證
    了我們的想法:使用者的文字評論訊息的確能夠用以改進推
    薦系統之效能。
    Reference: [1] G. Adomavicius and Y. Kwon. New recommendation techniques for
    multicriteria rating systems. IEEE Intelligent Systems, 22(3):48–55,
    2007.
    [2] H. Ahn, K. Kim, and I. Han. Mobile advertisement recommender system
    using collaborative filtering: Mar-cf. 2006.
    [3] D. Bridge and A. Waugh. Using experience on the read/write web:
    The ghostwriter system. In Proceedings of WebCBR: The Workshop
    on Reasoning from Experiences on the Web (Workshop Programme of
    the Eighth International Conference on Case-Based Reasoning), pages
    15–24, 2009.
    [4] R. Burke. Hybrid recommender systems: Survey and experiments. User
    modeling and user-adapted interaction, 12(4):331–370, 2002.
    [5] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and
    M. Sartin. Combining content-based and collaborative filters in an online
    newspaper. In Proceedings of ACM SIGIR Workshop on Recommender
    Systems, volume 60. Citeseer, 1999.
    [6] M. Fuchs and M. Zanker. Multi-criteria ratings for recommender systems:
    an empirical analysis in the tourism domain. In International
    Conference on Electronic Commerce and Web Technologies, pages
    100–111. Springer, 2012.
    [7] K. Ganesan and C. Zhai. Opinion-based entity ranking. Information
    Retrieval, 15(2):116–150, 2012.
    33
    [8] G. Huming and L. Weili. A hotel recommendation system based on
    collaborative filtering and rankboost algorithm. In 2010 second international
    conference on multimedia and information technology, 2010.
    [9] A. Levi, O. Mokryn, C. Diot, and N. Taft. Finding a needle in a haystack
    of reviews: cold start context-based hotel recommender system. In Proceedings
    of the sixth ACM conference on Recommender systems, pages
    115–122. ACM, 2012.
    [10] Y. Liu, X. Huang, A. An, and X. Yu. Modeling and predicting the
    helpfulness of online reviews. In Data Mining, 2008. ICDM’08. Eighth
    IEEE International Conference on, pages 443–452. IEEE, 2008.
    [11] C. D. Manning, P. Raghavan, and H. Sch¨utze. Introduction to Information
    Retrieval. Cambridge University Press, New York, NY, USA,
    2008.
    [12] M. P. O’Mahony and B. Smyth. Learning to recommend helpful hotel
    reviews. In Proceedings of the Third ACM Conference on Recommender
    Systems, pages 305–308. ACM, 2009.
    [13] S. Rendle. Factorization machines. In Proceedings of Data Mining,
    2010 IEEE 10th International Conference on, pages 995–1000. IEEE,
    2010.
    [14] S. Rendle. Factorization machines with libFM. ACM Transactions on
    Intelligent Systems and Technology, 3(3):57:1–57:22, May 2012.
    [15] G. Salton, A. Wong, and C.-S. Yang. A vector space model for automatic
    indexing. Communications of the ACM, 18(11):613–620, 1975.
    [16] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative
    filtering recommendation algorithms. In Proceedings of the 10th
    Internatio
    Description: 碩士
    國立政治大學
    資訊科學系碩士在職專班
    101971019
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101971019
    Data Type: thesis
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

    Files in This Item:

    File SizeFormat
    101901.pdf1089KbAdobe PDF324View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback