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研究生： 黃龍華
Wijaya, Aditya Utama
論文名稱： 基於項目的協同過濾推薦系統應用於短期項目之設計與評估
Designing and Evaluating Item-based Collaborative Filtering Recommendation Schemes for Short-period Items
指導教授： 雷松亞
Ray, Soumya
口試委員: 徐茉莉
Shmueli, Galit
林福仁
Lin, Fu-Ren
學位類別： 碩士
Master
系所名稱： 科技管理學院 - 國際專業管理碩士班
International Master of Business Administration(IMBA)
論文出版年： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 39
中文關鍵詞： 推薦系統 、以項目為基礎之協同過濾 、貼圖 、相似度測量 、個人化推薦 、非個人化推薦 、時間區間 、喜好
外文關鍵詞： recommendation system, item-based collaborative filtering, stickers, similarity measurement, personalized recommendations, non-personalized recommendations, time range, preference
相關次數： 點閱：3  下載：0 
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    In the last 20 years, recommendation system has been becoming more and more widely used in many web and mobile applications. It was started when Amazon popularized a recommendation technique called item-based collaborative filtering. This technique is fast, stable, and it performs well in most media sharing contexts. However, we found that there are some serious differences in recommending “stickers”, compared to traditional media items, like movies, songs, and so on. We have tried several approaches to improve recommendations in this context by comparing different similarity measurement methods, comparing personalized and non-personalized recommendations, and altering the time range used for generating the recommendation lists. We found that, in the situation where preference measurement does not have upper-bound, adjusted cosine similarity and cosine similarity methods perform better than Pearson correlation method. Meanwhile, in the situation where items have short-lived popularity period, straightforward personalized recommendations give bad accuracy. Finally, the personalized recommendations show performance improvement when generated using shorter time range.

    Abstract 1 Acknowledgement 2 1. Introduction 3 2. Study Setting: PicCollage 4 2.1. App Profile 4 2.2. Research Opportunities 5 2.3. Research Challenges 6 3. Overview of Recommendation Systems and Analysis 9 3.1. Collaborative Filtering Method 10 3.1.1. Item-Based Collaborative Filtering Technique 12 3.1.2. Weighted Average 15 3.2. Evaluate the Recommendation 16 3.3. Study Design and Evaluation 17 4. Methodology and Results 18 4.1. Data Extraction, Transformation, and Loading (ETL) 18 4.2. Data Exploration and Pre-Processing 20 4.3. Building the Recommendation Model 21 4.3.1. Constructing the Similarity Matrices 21 4.3.2. Personalized Recommendations 23 4.3.3. Evaluating the Personalized Recommendations 25 4.3.4. Non-Personalized Recommendations 27 4.3.5. Comparing Non-personalized and Personalized Recommendations 29 4.3.6. Using Shorter Time Period 30 5. Discussion of Contributions 32 6. Limitations 35 7. Conclusion 36 References 36

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