近年來，隨著電子商務涉及領域的拓展趨勢下，人類的生活型態與消費方式已逐漸改變。 推薦系統作為線上電影串流服務的核心技術，用於為使用者提供個人化的電影推薦，也隨之受到了領域內的廣泛討論。 在當前消費者對於隱私權愈趨重視的趨勢下，可解釋推薦系統能夠提供高品質推薦同時提供易於消費者理解的推薦解釋已成為產業界與學術界的研究熱點。 其中的推薦解釋方法要解決的問題是推薦系統的可解釋性，讓一般民眾能夠理解為什麼系統會將某個特定的產品推薦給消費者。 本文提出了一種用於商用電影推薦系統的視覺解釋方法，此方法專注於開發一套專門為一般民眾所設計的視覺可解釋推薦系統。

In recent years, e-commerce has gradually changed the way of life of human beings, with online movie streaming services emerging as one of the applications most intimately connected to our daily lives within the e-commerce domain. These services employ recommendation systems that utilize personal data, such as gender, age, location, and browsing history to provide users with personalized movie recommendations. However, the utilization of personal data in these systems has led to privacy concerns among customers. To alleviate this problem, we proposed a visual explanation method for an e-commerce movie recommendation system. Our research aims to design a visualization-based explanation method to show what user data are used in movie recommendations. This method not only enhances transparency but also reinforces user trust in the recommendation systems. Through a system evaluation, we demonstrate how our visualization design aids users in understanding the use of their data in the recommendation system, ultimately fostering a more trusting relationship with the system.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
Table of Contents v
List of Figures vii
List of Tables ix
CHAPTER 1 Introduction 1
CHAPTER 2 Related Work 7
2.1 Recommendation Systems 7
2.2 Explainable Artificial Intelligence 11
2.3 Explanations in Recommendation System 12
2.3.1 Non-visual Explanations 15
2.3.2 Visual Explanations 16
CHAPTER 3 System Design 22
3.1 User Requirements 22
3.2 Visualization Explanations Design 23
3.2.1 Movie Quick Review Section 28
3.2.2 Profile Characteristics Visualization Section 29
3.2.3 Preferred Movies Characteristics Visualization Section 31
3.3 System Design 33
3.3.1 Personalized Recommendation 33
3.3.2 Visual Explanations 34
3.4 Usage Scenario 35
CHAPTER 4 System Evaluation 40
4.1 Method 40
4.1.1 Participants 40
4.1.2 Study Design 41
4.1.3 Measurements 46
4.1.4 Study Procedure 50
4.2 Results 52
4.2.1 Explainability Assessment: Response Accuracy and Confidence 53
4.2.2 Explainability Assessment: Clarity and Sufficiency of Explanation 59
4.2.3 Trust Scale 61
4.2.4 System Usability Scale (SUS) 64
CHAPTER 5 Discussion 66
5.1 Potential to Meeting User Requirements 66
5.2 Limitations and Future Work 68
CHAPTER 6 Conclusion 71
CHAPTER 7 References 74

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