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題名 改良式協同過濾推薦系統之架構與評估
A framework and evaluation of recommendation system using modified collaborative filtering method
作者 張玉佩
貢獻者 李有仁
張玉佩
關鍵詞 推薦系統
協同過濾
資料稀疏性
冷開始
日期 2012
上傳時間 2-Sep-2013 16:02:19 (UTC+8)
摘要 協同過濾是電子商務中最常被使用也是最成功的推薦技術，但隨著電子商務的發展，網站使用者與商品數也迅速成長，使得使用者相關資料稀疏(Data sparsity)而嚴重影響推薦品質。對於新使用者與新商品，協同過濾也無法提供準確的推薦。為改善以上問題，本研究使用Lemire與Maclachlan (2005)所提出的Slope One演算架構及資料探勘方法中的單純貝式分類器(Naïve bayes classifier)來解決資料稀疏性和冷開始(Cold-start)問題。同時，考量到運算成本，將推薦系統架構分為離線預處理階段和線上預測階段，以避免當使用者數目和商品越來越大時運算成本超過實際可接受程度。
     本研究採用MovieLens資料庫的資料集，包含943位使用者與1,682部電影，共10萬筆評比資料，評比分數範圍從1到5分，其中每位使用者至少評比20部以上電影。實驗評估方法則採用平均絕對誤差(MAE)來計算本研究的推薦系統對消費者喜好預測的準確度。
     本研究希望所提出的個人化推薦系統能改善傳統協同過濾推薦系統的推薦品質，減少資料稀疏所造成的推薦誤差，更準確的推薦使用者感興趣的物品，以幫助使用者更有效率的進行線上消費，提高顧客滿意度與忠誠度，也提升電子商務網站營業效益。
參考文獻 任晓丽、刘鲁(民96)。推荐系统研究进展及展望。取自：中国科技论文在线，http://www.paper.edu.cn/index.php/default/releasepaper/content/200712-478
     張哲銘 (民92)。以使用者偏好分類為基礎之網際資源推薦系統(未出版之碩士論文)。國立台灣大學，台北市。
     黃君德 (民91)。電子商業網站產品推薦系統的研究與實作(未出版之碩士論文)。國立台灣大學，台北市。
     楊亨利、黃仁智(民97)。具整體觀點考量之推薦系統：以家庭親子為例，中華管理評論國際學報，11(3)，1-26。
     
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     Lemire, D., & Maclachlan, A. (2005). Slope one predictors for online rating-based collaborative filtering. Proceedings of SIAM Data Mining Conference, pp. 471-475.
     Li, Q., & Kim, B. M. (2003). Clustering Approach for Hybrid Recommender System. Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, pp. 33-38. doi:10.1109/WI.2003.1241167
     Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76-80. doi:10.1109/MIC.2003.1167344
     Melville, P., & Sindhwani, V. (2010). Recommender systems. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 829-838). Boston, MA: Springer.
     Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on Web usage mining. Communications of the ACM, 43(8), 142-151. doi:10.1145/345124.345169
     Papagelis, M., & Plexousakis, D. (2005). Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence, 18(7), 781-789. doi:10.1016/j.engappai.2005.06.010
     Pazzani, M. J. (1999). A Framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408. doi:10.1023/A:1006544522159
     Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., & Riedl, J. (2002). Getting to know you: Learning new user preferences in recommender systems. Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127-134. doi:10.1145/502716.502737
     Resnick, P., Iacovou , N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering of Netnews. Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175-186. doi:10.1145/192844.192905
     Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58. doi:10.1145/245108.245121
     Ricci, F. (2002). Travel recommender systems. IEEE Intelligent Systems, 17(6), 55-57.
     Salton, G., & McGill, M. J. (1986). Introduction to Modern Information Retrieval. New York, NY: McGraw-Hill.
     Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000a). Application of dimensionality reduction in recommender system -- a case study (Technical Reports 00-043). Retrieved from University of Minnesota Computer Science Technical Reports Archive website: http://www.cs.umn.edu/tech_reports_upload/tr2000/00-043.pdf
     Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000b). Analysis of recommendation algorithms for e-commerce. Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158-167. doi:10.1145/352871.352887
     Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, pp. 285-295. doi:10.1145/371920.372071
     Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., & Riedl, J. (1998). Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. Proceedings of the 1998 ACM conference on Computer supported cooperative work, pp. 345-354. doi:10.1145/289444.289509
     Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158-166. doi:10.1145/336992.337035
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     Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and Metrics for Cold-Start Recommendations. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253-260. doi:10.1145/564376.564421
     Shin, Y., & Liu, D. (2008). Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications, 35(1-2), 350-360. doi:10.1016/j.eswa.2007.07.055
     Su, J. H., Wang, B. W., Hsiao, C. Y., & Tseng, V. S. (2010). Personalized rough-set-based recommendation by integrating multiple contents and collaborative information. Information Sciences, 180(1), 113-131. doi:10.1016/j.ins.2009.08.005
     Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques, Advances in Artificial Intelligence, 2009, 1-19. doi:10.1155/2009/421425
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     Xue, G., Lin, C., Yang, Q., Xi, W., Zeng, H., Yu, Y., & Chen, Z. (2005). Scalable collaborative filtering using cluster-based smoothing. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 114-121. doi:10.1145/1076034.1076056
     Yuan, X., & Wu, P. (2012). Content-based recommendation model in micro-blogs community. Proceedings of 2012 International Conference on Management of e-Commerce and e-Government, pp. 165-168. doi:10.1109/ICMeCG.2012.40
     Zhang, D. J. (2009). An item-based collaborative filtering recommendation algorithm using slope one scheme smoothing. In Li, M., Yu F., Shu, J., & Chen, Z. G. (Eds.), Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security: Vol. 2. (pp. 215-217). Washington, DC: IEEE Computer Society. doi:10.1109/ISECS.2009.173
描述 碩士
國立政治大學
資訊管理研究所
99356035
101
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0993560351
資料類型 thesis
dc.contributor.advisor 李有仁zh_TW
dc.contributor.author (Authors) 張玉佩zh_TW
dc.creator (作者) 張玉佩zh_TW
dc.date (日期) 2012en_US
dc.date.accessioned 2-Sep-2013 16:02:19 (UTC+8)-
dc.date.available 2-Sep-2013 16:02:19 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2013 16:02:19 (UTC+8)-
dc.identifier (Other Identifiers) G0993560351en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59303-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 99356035zh_TW
dc.description (描述) 101zh_TW
dc.description.abstract (摘要) 協同過濾是電子商務中最常被使用也是最成功的推薦技術，但隨著電子商務的發展，網站使用者與商品數也迅速成長，使得使用者相關資料稀疏(Data sparsity)而嚴重影響推薦品質。對於新使用者與新商品，協同過濾也無法提供準確的推薦。為改善以上問題，本研究使用Lemire與Maclachlan (2005)所提出的Slope One演算架構及資料探勘方法中的單純貝式分類器(Naïve bayes classifier)來解決資料稀疏性和冷開始(Cold-start)問題。同時，考量到運算成本，將推薦系統架構分為離線預處理階段和線上預測階段，以避免當使用者數目和商品越來越大時運算成本超過實際可接受程度。
     本研究採用MovieLens資料庫的資料集，包含943位使用者與1,682部電影，共10萬筆評比資料，評比分數範圍從1到5分，其中每位使用者至少評比20部以上電影。實驗評估方法則採用平均絕對誤差(MAE)來計算本研究的推薦系統對消費者喜好預測的準確度。
     本研究希望所提出的個人化推薦系統能改善傳統協同過濾推薦系統的推薦品質，減少資料稀疏所造成的推薦誤差，更準確的推薦使用者感興趣的物品，以幫助使用者更有效率的進行線上消費，提高顧客滿意度與忠誠度，也提升電子商務網站營業效益。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
     第一節 研究背景 1
     第二節 研究動機 2
     第三節 研究目的 3
     第四節 研究流程 3
     第二章 文獻探討 5
     第一節 推薦系統 5
     2.1.1 推薦系統定義 5
     2.1.2 推薦系統分類 7
     第二節 協同過濾 10
     2.2.1 協同過濾定義 10
     2.2.2 協同過濾限制 11
     2.2.3 相似度測量 14
     2.2.4 產生推薦 17
     第三章 研究架構 18
     第一節 系統架構 18
     第二節 Slope one 20
     第三節 單純貝式分類 20
     3.3.1 貝式分類器 20
     3.3.2 單純貝式分類器 21
     第四章 實驗設計與結果評估 22
     第一節 實驗設計 23
     4.1.1 相似度選擇 23
     4.1.2 評分預測計算方式 23
     4.1.3 實驗流程 24
     第二節 資料值填補方式比較 29
     第三節 不同比例冷開始使用者 30
     第四節 不同比例新使用者 31
     第五節 使用者分群 33
     第五章 結論與建議、研究限制與未來研究方向 35
     第一節 結論與建議 35
     第二節 研究限制與未來研究方向 36
     參考文獻 38
     附錄一 42
     附錄二 45
     表目錄
     表 2-1 推薦系統相關定義 5
     表 2-2 Slope one評分極端值示意 12
     表 2-3 皮爾森係數產生分母為零之錯誤 15
     表 4-1 評分預測計算方式 24
     表 4-2 填補後資料稀疏程度 30
     表 5-1 實驗結果總結 35
     圖目錄
     圖 1-1 研究流程 4
     圖 2-1 線性組合圖式 9
     圖 2-2 循序組合圖式 9
     圖 2-3 協同過濾推薦系統處理程序 11
     圖 2-4 Slope one 設計基礎概念示意 12
     圖 2-5 餘弦相似度問題示意 16
     圖 3-1 研究架構 18
     圖 4-1-1 不同相似度之CF結果預測準確度衡量比較 23
     圖 4-1-2 第一階段實驗流程圖 25
     圖 4-1-3 第二階段實驗流程圖 26
     圖 4-1-4 第三階段實驗流程圖Ⅰ-以Slope one填補舊使用者評分矩陣 27
     圖 4-1-5 第三階段實驗流程圖Ⅱ-以單純貝式分類器填補舊使用者評分矩陣 27
     圖 4-1-6 使用者分群示意圖 28
     圖 4-1-7 第四階段實驗流程圖Ⅰ-以Slope one填補使用者評分矩陣 28
     圖 4-1-8 第四階段實驗流程圖Ⅱ-以單純貝式分類填補使用者評分矩陣 28
     圖 5-1 修改後研究架構 36
     圖 4-2-1 以Slope one填補評分矩陣空缺值的CF預測準確度衡量比較 45
     圖 4-2-2 以單純貝式分類器填補評分矩陣空缺值的CF預測準確度衡量比較 45
     圖 4-2-3 SOCF、NBCF和傳統CF的預測準確度比較 46
     圖 4-3-1 20%冷開始使用者之預測準確度比較 46
     圖 4-3-2 40%冷開始使用者之預測準確度比較 47
     圖 4-3-3 60%冷開始使用者之預測準確度比較 47
     圖 4-3-4 80%冷開始使用者之預測準確度比較 48
     圖 4-3-5 100%冷開始使用者之預測準確度比較 48
     圖 4-4-1 5%新使用者狀況下填補20筆評分資料之預測準確度比較 49
     圖 4-4-2 5%新使用者狀況下填補40筆評分資料之預測準確度比較 49
     圖 4-4-3 5%新使用者狀況下填補60筆評分資料之預測準確度比較 49
     圖 4-4-4 5%新使用者狀況下填補80筆評分資料之預測準確度比較 50
     圖 4-4-5 5%新使用者狀況下填補100筆評分資料之預測準確度比較 50
     圖 4-4-6 5%新使用者狀況下填補20至100筆評分時SOCF-PCC之預測準確度 50
     圖 4-4-7 10%新使用者狀況下填補20筆評分資料之預測準確度比較 51
     圖 4-4-8 10%新使用者狀況下填補40筆評分資料之預測準確度比較 51
     圖 4-4-9 10%新使用者狀況下填補60筆評分資料之預測準確度比較 51
     圖 4-4-10 10%新使用者狀況下填補80筆評分資料之預測準確度比較 52
     圖 4-4-11 10%新使用者狀況下填補100筆評分資料之預測準確度比較 52
     圖 4-4-12 10%新使用者狀況下填補20至100筆評分時SOCF-PCC之預測準確度 52
     圖 4-4-13 15%新使用者狀況下填補20筆評分資料之預測準確度比較 53
     圖 4-4-14 15%新使用者狀況下填補40筆評分資料之預測準確度比較 53
     圖 4-4-15 15%新使用者狀況下填補60筆評分資料之預測準確度比較 53
     圖 4-4-16 15%新使用者狀況下填補80筆評分資料之預測準確度比較 54
     圖 4-4-17 15%新使用者狀況下填補100筆評分資料之預測準確度比較 54
     圖 4-4-18 15%新使用者狀況下填補20至100筆評分時SOCF-PCC之預測準確度 54
     圖 4-4-19 20%新使用者狀況下填補20筆評分資料之預測準確度比較 55
     圖 4-4-20 20%新使用者狀況下填補40筆評分資料之預測準確度比較 55
     圖 4-4-21 20%新使用者狀況下填補60筆評分資料之預測準確度比較 55
     圖 4-4-22 20%新使用者狀況下填補80筆評分資料之預測準確度比較 56
     圖 4-4-23 20%新使用者狀況下填補100筆評分資料之預測準確度比較 56
     圖 4-4-24 20%新使用者狀況下填補20至100筆評分時CF-PIP之預測準確度 56
     圖 4-5-1 將使用者分為3個群集狀況下之預測準確度比較 57
     圖 4-5-2 將使用者分為4個群集狀況下之預測準確度比較 57
     圖 4-5-3 將使用者分為5個群集狀況下之預測準確度比較 58		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0993560351en_US
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 協同過濾zh_TW
dc.subject (關鍵詞) 資料稀疏性zh_TW
dc.subject (關鍵詞) 冷開始zh_TW
dc.title (題名) 改良式協同過濾推薦系統之架構與評估zh_TW
dc.title (題名) A framework and evaluation of recommendation system using modified collaborative filtering methoden_US
dc.type (資料類型) thesisen
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     黃君德 (民91)。電子商業網站產品推薦系統的研究與實作(未出版之碩士論文)。國立台灣大學，台北市。
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