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論文中文名稱:整合內容導向式方法與混合式協同過濾之電影推薦 [以論文名稱查詢館藏系統]
論文英文名稱:Integration of Content-based approach and Hybrid Collaborative Filtering for Movie Recommendation [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與運籌管理研究所
畢業學年度:101
出版年度:102
中文姓名:李佳馨
英文姓名:Chia-Hsing Lee
研究生學號:100938012
學位類別:碩士
語文別:中文
口試日期:2013-07-03
論文頁數:55
指導教授中文名:翁頌舜
口試委員中文名:吳瑞堯;蕭瑞祥
中文關鍵詞:協同過濾內容導向式方法矩陣分解冷啟動
英文關鍵詞:Collaborative FilteringContent-based ApproachMatrix FactorizationCold Start
論文中文摘要:隨著電子商務規模不斷擴大,企業為了節省消費者的搜尋時間與成本,個人化推薦系統應運而生。個人化推薦系統的核心技術中,應用最為廣泛的推薦方法之一的協同過濾,仍然存在著幾個問題。第一為評分矩陣稀疏問題(Sparse),難以找到相似的使用者,影響預測的準確度,第二為冷啟動(Cold Start),包含新使用者與新項目的問題,缺乏評分依據,導致無法預測使用者的喜好程度進行推薦。
本研究模擬推薦系統所面臨的真實環境,在評分矩陣稀疏的情況下,考量新使用者與新電影的因素,以電影屬性為基礎的內容導向式方法(Content-based approach)預測一般使用者對新電影的評比,並且修正協同過濾(Collaborative Filtering)傳統的相似度計算結合矩陣分解(Matrix Factorization)方法預測新使用者與一般使用者尚未評分之電影評比。在不同稀疏程度的資料集下,以本研究方法對整體評分預測有較低的MAE誤差值,代表預測的分數越接近實際的評分也較符合使用者的喜好,實驗證實本研究方法在稀疏評分矩陣的預測準確率較高且優於傳統的協同過濾方法。
論文英文摘要:As the scale of e-commerce continues to expand, personalized recommendation systems have been developed for general users in the hope of saving their search cost and time. In the core methods of personalized recommendation systems, collaborative filtering, one of the most widely-used recommended methods, still leaves two major problems. One is sparsity problem, the difficulty of finding similar users results in poor accuracy. The other is cold start, new users and new items make it hardly possible to estimate the preferences because of the lack of past ratings.
This work simulates a real environment for movie recommendation. In the case of considering the factors of the new users and new movies in the sparse rating matrix, we conduct a content-based approach based on movie genre to predict user ratings on new movies. Furthermore, we integrate the modification of similar measures in memory-based collaborative filtering with matrix factorization(model-based collaborative filtering). In experiments, we observe our methodology brought out a lower MAE in overall rating prediction. Finally, our approach has been shown to have better recommendation quality than basic collaborative filtering in different sparsity level dataset.
論文目次:中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
表 目 錄 vi
圖 目 錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章 文獻探討 4
2.1 推薦系統 4
2.1.1 人口統計過濾(Demographic Filtering) 5
2.1.2 內容導向式推薦(Content-based Approach) 6
2.1.3 協同過濾推薦(Collaborative Filtering) 8
2.1.4 混合導向式推薦(Hybrid-based Approach) 9
2.2 記憶導向式協同過濾推薦(Memory-based Collaborative Filtering) 10
2.2.1 使用者導向式協同過濾(User-based Collaborative Filtering) 10
2.2.2 項目導向式協同過濾(Item-based Collaborative Filtering) 12
2.3 模型導向式協同過濾推薦(Model-based Collaborative Filtering) 12
2.3.1 基礎矩陣分解模型(Basic Matrix Factorization Model) 13
2.3.2 基準預測模型(Baseline Matrix Factorization Model) 14
第三章 研究方法 16
3.1 研究架構 16
3.2 預測模組功能介紹 17
3.2.1 以電影屬性為基礎的內容導向式方法(Content-based Approach) 17
3.2.2 混合式協同過濾方法(Collaborative Filtering) 21
第四章 實驗設計與結果 26
4.1 實驗環境 26
4.2 資料來源與資料前處理 26
4.3 評估指標 27
4.4 實驗情境與設計 27
4.4.1 情境說明 27
4.4.2 實驗設計 28
4.4.3 實驗相關參數設定 31
4.5 實驗分析與結果 32
第五章 結論 49
5.1 研究結論與貢獻 49
5.2 未來方向與建議 50
參考文獻 51
論文參考文獻:1. 郭秉仁,基於個人本體論與MapReduce技術之圖書推薦系統,碩士論文,國立中興大學資訊科學與工程學系所,2012年。
2. 黃威豪,基於電影屬性之模糊推論推薦模式與評估,碩士論文,輔仁大學資訊管理學系所,2011年。
3. Adomavicius, G. & Tuzhilin, E., “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, 2005, pp. 734-749.
4. Alspector, J. A., Kolcz, A. & Karunanithi, N., “Comparing feature-based and clique-based user models for movie selection,” Proceedings of the 3rd ACM Conference on Digital Libraries, Pittsburgh, Pennsylvania, 1998, pp. 11-18.
5. Armstrong, R., Freitag, D., Joachims, T. & Mitchell, T., “WebWatcher: A Learning Apprentice for the World Wide Web,” Proceedings of Fourteenth International Joint Conference on Artificial Intelligence, Stanford, CA, 1995.
6. Balabanovic, M. & Shohan, Y., “Fab: content-based, collaborative recommendation,” Communications of ACM, vol. 40, no. 3, 1997, pp. 66-72.
7. Basu, C. & Cohen, W. W., “Recommendation as Classification: Using social and content-based information in recommendation,” Proceedings of the Fifteenth National Conference on Artificial Intelligence, 1998.
8. Breese, J. S., Heckerman, D. & Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, 1998, pp. 43-52.
9. Burke, R., “Hybrid Recommender systems: survey and experiment,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, 2002, pp. 331-370.
10. Choi, k., Yoo, D., Kim, G. & Suh, Y., “A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis,” Electronic Commerce Research and Applications, vol. 11, no. 4, 2012, pp. 309-317.
11. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D. & Sartin, M., “Combining Content based and Collaborative Filters in an Online Newspaper,” Proceedings of the ACM SIGIR '99 Workshop on Recommender Systems: Algorithms and Evaluation, University of California, Berkeley, 1999.
12. Connor, M. & Herlocker, J., “Clustering items for collaborative filtering,” 2001.
13. Feng, X., Ming, X. & Zhen, C., “RBRA: A Simple and Efficient Rating-Based Recommender Algorithm to Cope with Sparsity in Recommender Systems,” Proceedings of the 2012 26th International Conference on Advanced Information Networking and Applications Workshops, Washington, DC, 2012, pp. 306-311.
14. Goldberg, D., Nichols. D., Oki, B. M. & Terry, D., “Using Collaborative Filtering to Weave an Information Tapestry,” Communications of the ACM - Special issue on information filtering, vol. 35, no. 12, 1992, pp. 61-70.
15. Herrada, O. C., “Music Recommendation and discovery in the long tail,” Berlin: Springer, 2008, pp. 28-30.
16. Hu, R. & Pu, P., “A Comparative User Study on Rating vs. Personality Quiz based Preference Elicitation Methods,” Proceedings of the 14th international conference on Intelligent user interfaces, New York, NY, 2009, pp. 367-372.
17. Huang, Z., Chen, H. & Zeng, D., “Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering,” ACM Transactions on Information Systems, vol. 22, no. 1, 2004, pp. 116-142.
18. Huy, N. & Tien, D., “A Modified Regularized Non-Negative Matrix Factorization for MovieLens,” 2012 IEEE RIVF International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Ho Chi Minh City, Vietnam, 2012, pp. 1-5.
19. Jannach, D., Zanker, M., Felfernig, A. & Friedrich, G., “Recommender Systems An Introduction,” Cambridge: Cambridge University Press, 2010, pp. 51-79.
20. Kim, J. K., Kim, H. K., Oh, H. Y. & Ryu, Y. U., “A group recommendation system for online communities,” International Journal of Information Management, vol. 30, no. 3, 2010, pp. 212-219.
21. Kim B. M., Li Q., Kim, S. G. & Kim, J. Y., “A New Approach for Combining Content-based and Collaborative Filters,” Journal of Intelligent Information Systems archive, vol. 27, no. 6, 2006, pp. 79-91.
22. Koren, Y., Bell, R. M. & Volinsky, C., “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, 2009, pp. 30-37.
23. Krulwich, B., “LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data,” Artificial Intelligence Magazine, vol. 18, no. 2, 1997, pp. 37-45.
24. Krulwich, B. & Burkey, C., “The InfoFinder agent: Learning user interests through heuristic phrase extraction,” IEEE Expert, vol. 12, no. 5, 1997, pp. 22-27.
25. Lang, K., “NewsWeeder: Learning to filter netnews,” Proceedings of the 12th International Conference on Machine Learning, San Francisco, CA, 1995. pp. 331-339.
26. Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S. & Duri, S. S., “Personalization of Supermarket Product Recommendations,” Data Mining and Knowledge Discovery, vol. 5, no. 1-2, 2001, pp. 11-32.
27. Mirza, B. J., Keller, B. J. & Ramakrishnan, N., “Studying recommendation algorithms by graph analysis,” Intelligent Information Systems, vol. 20, no. 2, 2003, pp. 131-160.
28. Paterek, A., “Improving regularized singular value decomposition for collaborative filtering,” Proceedings of the 13th ACM Conference on Knowledge Discovery and Data Mining, 2007, pp. 39-42.
29. Pazzani, M., “A framework for collaborative, content-based and demographic filtering,” Artificial Intelligence Review - Special issue on data mining on the Internet, vol. 13, no. 5-6, 1999, pp. 393-408.
30. Peddy, C. C. & Armentrout, D., “Building Solutions with Microsoft Commerce Server2002,” Microsoft Press, 2003.
31. Popescul, A., Ungar, L. H., Pennock, D. M. & Lawrence, S., “Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments,” Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001, pp. 437-444.
32. Resnick, P. & Varian, H. R., “Recommender Systems,” Communications of the ACM, vol.40, no. 3, 1997, pp. 56-58.
33. Rich, E., “User modeling via stereotypes,” Cognitive Science, vol. 3, no. 4, 1979, pp. 329–354.
34. Salakhutdinov, R. & Mnih, A., “Probabilistic matrix factorization,” Proceedings of Advances in Neural Information Proceeding Systems 20, 2008, pp. 1257-1264.
35. Sarwar, B., Karypis, G., Konstan, J. & Riedl, J., “Analysis of Recommendation Algorithms for E-Commerce,” Proceedings of the 2nd ACM conference on Electronic commerce, New York, NY, 2000, pp. 158-167.
36. Savia, E., Puolam‥aki, K. & Kaski, S., “Latent grouping models for user preference prediction,” Machine Learning, vol. 74, no. 1, 2009, pp.75–109.
37. Schafer J. B., Konstan, J. & Riedl, J., “E-commerce recommendation applications,” Data Mining and Knowledge Discovery, vol. 5, no. 1-2, 2001, pp. 115-153.
38. Shardanand, U. & Maes, P., “Social Information Filtering: Algorithms for Automating Word of Mouth,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, 1995, pp. 210-217.
39. Vozalis, M. & Margaritis, K. G., “Collaborative filtering enhanced by demographic correlation,” Proceedings of the AIAI Symposium on Professional Practice in AI of the 18th World Computer Congress, Toulouse, France, 2004.
40. Wang, M. J. & Han, J. T., “Collaborative filtering recommendation based on item rating and characteristic information prediction,” Proceedings of the 2nd International Conference on Consumer Electronics Communications and Networks, 2012, pp. 214-217.
41. Wu, J., “Binomial matrix factorization for discrete collaborative filtering,” Proceedings of IEEE International Conference on Data Mining, 2009, pp. 1046-1051.
42. MBA智庫百科-個性化推薦系統
http://wiki.mbalib.com/zh-tw/%E4%B8%AA%E6%80%A7%E5%8C%96%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F.
43. 維基百科- GroupLens
http://zh.wikipedia.org/wiki/GroupLens.
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