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研究生:鈕韻芳
研究生(外文):Niu, Yun-Fang
論文名稱:整合定錨理論與偏好穩定度之互動式電影推薦
論文名稱(外文):Integrating the anchor theory with preference stability for interactive movie recommendations
指導教授:吳怡瑾吳怡瑾引用關係
指導教授(外文):Wu, I-Chin
口試委員:陳子立楊千
口試委員(外文):Chen, Tzu-LiYang, Chyan
口試日期:2012-06-27
學位類別:碩士
校院名稱:輔仁大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:108
中文關鍵詞:推薦系統混合式過濾推薦定錨理論層級分析法偏好穩定度
外文關鍵詞:Recommendation systemHybrid filteringAnchor theoryAnalytic Hierarchy Process (AHP)Preference stability
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近年來由於推薦系統極具商業價值,許多電子商務網站紛紛採用協同式過濾推薦系統,根據偏好相似的顧客,將其喜歡的商品推薦給其他使用者。然而,隨著顧客與產品的增加,協同式過濾推薦因矩陣稀疏與冷啟動的問題,無法準確地推薦該顧客真正喜歡的商品,導致推薦的準確率下降。此外,傳統的推薦策略,只考慮商品項目的屬性與顧客的相似度,其忽略顧客的內在偏好會隨顧客的選擇過程、經驗,而發展個人偏好。因此我們結合以定錨理論、偏好穩定度為基礎的互動式推薦過程,與模糊混合式過濾推薦(Genre-based fuzzy inference hybrid approach),進一步地捕捉使用者的內在偏好,提升推薦方法的準確率,並探討各種推薦策略對不同偏好狀態使用者的影響,找出不同狀態的使用者較適合的推薦方法。研究結果顯示,本研究提出之方法擁有較好的推薦效果,能有效地過濾使用者不喜歡的電影,並更準確地篩選出符合使用者真正偏好的電影,且其特別適用於預測偏好不穩定的人的偏好。
Recommendation techniques are utilized in electronic commerce because of their potential commercial values. Many e-commerce sites employ collaborative filtering techniques to provide recommendations to customers based on the preferences of similar users. However, as the numbers of customers and products increase, the prediction accuracy of collaborative filtering algorithms declines because of sparse ratings and cold-start. In addition, the traditional recommended approaches just consider the item’s attribute and the preference similarities between users; however, they are not concerned that users’ preferences may be developed with growth in their familiarity with or experience during choice or preference elicitation. In this work, we propose an interactive anchoring process, i.e., a pair-wise preference comparison process with the G-Fuzzy hybrid filtering approach to capture the user’s preferences of movie genres more naturally and then achieve effective interactive recommendations. Then, we also discuss the influence of different types of user’s preference for developing the recommendation strategies. The experiment results show that the proposed Anchor-based hybrid filtering approach can achieve effective recommendations especially for the user who has the unstable preference of movie genres. Moreover, the Anchor-based hybrid filtering approach can effectively capture the user’s preference naturally and filter out the user’s disliked movies.
Content
List of Table vi
List of Figure viii
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation and Objective 2
Chapter 2 Related works 5
2.1 Anchor theory 5
2.2 Preference development 9
2.3 Recommendation system 11
2.3.1 Content-based recommendation 11
2.3.2 Collaborative filtering recommendation 12
2.3.3 Hybrid filtering recommendation 14
2.4 Analytic Hierarchy Process (AHP) 16
Chapter 3 The Anchor-based Hybrid Filtering Framework 19
3.1 The Research Problems 19
3.2 The iMovie System Framework 21
3.3 The Movie Preference Types 23
Chapter 4 The Anchor-based Hybrid Approach for Recommendation 25
4.1 Applying AHP method for the anchoring process 25
4.2 The Interactive Recommendation Procedure and the iMovie Interface 36
Chapter 5 Experimental Design 43
5.1 The Experimental dataset 43
5.2 Evaluation index 44
5.3 Experiment design 44
5.3.1 Experimental methods 44
5.3.2 The experimental process 49
Chapter 6 Experiment results 53
6.1 Evaluation of the subject’s stability 53
6.2 Evaluation of Filtering Results by Genre Preference Ratio 54
6.2.1 Experiment 1: The Genre-based fuzzy inference method 54
6.2.2 Experiment 2: The Anchor-based hybrid filtering method 61
6.2.3 Comparisons among Methods in Stage one 67
6.3 Evaluation of Recommendation Results by Precision 80
6.3.1 Experiment 1: The Genre-based fuzzy inference method 80
6.3.2 Experiment 2: The Anchor-based hybrid filtering method 84
6.3.3 Comparisons among Methods in Stage Two 89
Chapter 7 Conclusions and future researches 93
7.1 Overall Discussions 93
7.2 Future works 96
References 97
Appendix A The Genre-based fuzzy inference 105
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