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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/68621
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/68621

    Title: Improving the Effectiveness of Experiential Decisions by Recommendation Systems
    Authors: 林靖;許建隆;李有仁
    Lin, Arthur J.;Hsu, Chien-Lung;Li, Eldon Y.
    Contributors: 資管系
    Keywords: Recommendation system;Experiential decision;Multilayer perception model;Neural network system;Collaborative filtering system
    Date: 2014.08
    Issue Date: 2014-08-12 15:46:21 (UTC+8)
    Abstract: Providing experience-oriented offerings through e-commerce is an issue increasing critical in the growing commoditization of e-commercial services. The high accuracy of predictions rendered by Recommendation System (RS) technologies has strengthened the opportunities for experience-oriented offerings, making RS application an effective way of assisting consumers in online decision-making. This study proposes a RS for movie lovers using neural networks in collaborative filtering systems for consumers’ experiential decisions. The experimental results reveal that it not only improves the accuracy of predicting movie ratings but also increases data transfer rates and provides richer user experiences.
    Relation: Expert Systems with Applications, 41(10), 4904-4914
    Source URI: http://dx.doi.org/10.1016/j.eswa.2014.01.035
    Data Type: article
    Appears in Collections:[資訊管理學系] 期刊論文

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