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    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:
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    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:
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    [7] K. Ganesan and C. Zhai. Opinion-based entity ranking. Information
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    [8] G. Huming and L. Weili. A hotel recommendation system based on
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    [13] S. Rendle. Factorization machines. In Proceedings of Data Mining,
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    [14] S. Rendle. Factorization machines with libFM. ACM Transactions on
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    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

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