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研究生： 施雅煌
Ya-Huang Shih
論文名稱： 新產品推薦系統之延伸探討
Extending Traditional Collaborative Filtering with Attributes Extraction to Recommend New Products
指導教授： 許秉瑜
Ping-Yu Hsu
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 企業管理學系
Department of Business Administration
畢業學年度： 92
語文別： 英文
論文頁數： 48
外文關鍵詞： attribute extraction, collaborative filtering, Recommender system
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    • In the E-commerce, the recommendation is aimed at suggesting products to customers and providing consumers with information to help them decide which products to purchase. There are mainly three kinds of methods in a recommendation system in the previous literatures：Contents-based, Collaborative-filtering and Hybrid recommendation.
    Collaborative filtering is the most successful recommender system in both research and applications such as information filtering and E-commerce. However, it is still has restriction in recommending new items. In this paper, we focus on only Collaborative-filtering recommendation and try to remedy its restriction of implementations. We introduce a novel expansion approach, called the attribute-based mechanism that is based on the architecture of traditional collaborative-filtering recommendation and connected with the technique of attribute extraction. Our approach considers the purpose of recommendation not only as the promotion of existing items, but also as looking for the potential preference of users, who are expected to promote new items.
    Three main contributions can be presented：first, we offer other perspective to think about the nature of recommending system. Differing from the traditional CF approach emphasizing the prediction of the behavior of new consumers, this mechanism stresses the maintenance of the satisfaction of existing customers. Besides, we eliminate the recommendation limitation of the traditional CF approach through considering the purchasing motive of customers.
    Second, the attribute-based mechanism combines aspects of collaborative filtering and attribute extraction to recommend new items for a user based on their prior purchase behavior. Analysis results obtained during the experiments have shown that most users’ evaluations are higher than the mean.
    Third, we provide comparative results on the impact of parameters like the number of recommending items, the value of attribute threshold, etc.

    Abstract III List of Figures VI List of Tables VII Chapter 1. Introduction 1 Chapter 2. Related work 4 2.1. Categories of collaborative-filtering recommendation 4 2.2. Challenges of Memory-based algorithm 6 Chapter 3. Problem definition 8 Chapter 4. Methodology 10 4.1. Data representation 10 4.2. Neighborhood formation 11 4.3. Recommendation 13 4.4. Attribute extraction 14 4.4.1 Attribute reduction 15 4.4.2 Attribute generalization 16 4.5. Rules generation 17 Chapter 5. Evaluation 18 5.1. Data set 18 5.2. Attribute reduction 19 5.3. Key parameters 20 5.4. Metrics 20 5.5. Experiments with K value 22 5.6. Experiments with N value 23 5.7. Experiments with T value 25 Chapter 6. Conclusions 28 References 30 Appendix A. Description of the original attributes of item 33 Appendix B. The result of experiments with N value 34 Appendix C. The result of experiments with T value 39

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