臺灣博碩士論文加值系統

在協同過濾推薦系統中，使用者會對他們所購買的產品提供評分，藉由學習使用者們過去的交易、評分行為，推薦系統演算法可以推薦符合使用者們喜好的產品給使用者們。然而，產品的數目通常十分龐大，這也導致推薦系統的整體效能非常低落，因為在推薦每一件產品前，都需要考慮該名使用者對所有產品的喜好程度。因此，我們提出了一個新方法，將自建構分群演算法應用於推薦系統，以減少與產品的數量相關的維度，並藉此提升整體效能。其概念是將相似的產品集中在一個產品群；而不相似的產品則分到不同的產品群。在我們的方法中，推薦系統在運算過程中，可以改為以產品群為單位進行運算。最後，再透過反轉換，將產品群喜好列表，轉回產品喜好列表，並提供給每名使用者。我們所提出的方法，能使推薦系統的整體運算時間大幅的減少，並保持原先的高準確度。實驗結果証明，我們的推薦系統在效能上，較未經過維度縮減的推薦系統優良。

In collaborative filtering based recommender systems, products are regarded as features and users are required to provide rating scores to the products they have purchased. By learning from the rating scores, such a recommender system can recommend interesting products to the users. However, there are usually quite a lot of products involved and it would be very inefficient if every product needs to be considered before making any recommendations. We propose a novel approach which applies a self-constructing clustering algorithm to reduce the dimensionality related to the number of products. Similar products are grouped in a cluster and dissimilar products are dispatched in different clusters. Recommendation work is then done with the resulting clusters. Finally, re-transformation is performed and a preference list about the products is offered to each user. With the proposed approach, the processing time for making recommendations is much reduced. Experimental results show that the efficiency of the recommender systems is greatly improved without the degradation of the recommendation quality.

中文論文審定書+i
英文論文審定書+ii
誌謝+iii
中文摘要+iv
ABSTRACT+v
CONTENTS+vi
LIST OF FIGURES+viii
LIST OF TABLES+ix
Chapter 1 Introduction+1
1.1 研究背景+1
1.2 推薦系統+2
1.3 問題描述+6
1.4 論文架構+6
Chapter 2 文獻回顧+8
2.1 近年的內容導向的推薦系統介紹+8
2.2 近年的協同式過濾推薦系統介紹+9
2.3 ItemRank+12
Chapter 3 基於分群的協同過濾式推薦系統+14
3.1 自建構分群演算法(Self-Constructing Clustering)+15
3.2 第一階段：使用者標籤+19
3.3 第二階段：維度縮減+20
3.4 第三階段：建立關聯圖+22
3.5 第四階段：隨機移動+24
3.6 第五階段：反轉換+25
3.7 範例+27
Chapter 4 實驗結果+32
Chapter 5 結論與未來方向+41
5.1 結論+41
5.2 未來方向+41
REFERENCE+42

[1]C. Porcel, J. Moreno, E. Herrera-Viedma, “A multi-disciplinar recommender system to advice research resources in university digital libraries,” Expert Systems with Applications, vol. 36, issue. 10, pp. 12520–12528, 2009.
[2]F. Ricci, L. Rokach, B. Shapira, “Introduction to recommender systems handbook,” Recommender Systems Handbook, Springer, 2011.
[3]Q. Liu, E. Chen, H. Xiong, C. H. Q. Ding, J. Chen, “Enhancing collaborative filtering by user interest expansion via personalized ranking,” IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 42, no. 1, pp. 218–233, 2012.
[4]Y. Cai, H. fung Leung, Q. Li, H. Min, J. Tang, J. Li, “Typicality-based collaborative filtering recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, issue.3, pp. 766–779, 2014.
[5]A. Gatzioura, M. S`anchez-Marr`e, “A case-based recommendation approach for market basket data,” IEEE Intelligent Systems, vol. 30, issue. 1, pp. 20–27, 2014.
[6]M. Balabanovi´c, Y. Shoham, “Fab: Content-based, collaborative recommendation,” Communications of ACM, vol. 40, issue. 3, pp. 66–72, 1997.
[7]P. Melville, R. J. Mooney, R. Nagarajan, “Content-boosted collaborative filtering for improved recommendations,” in: 18th National Conference on Artificial Intelligence, Edmonton, Canada, pp. 187–192, 2002.
[8]S.-L. Huang, “Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods,” Electronic Commerce Research and Applications, vol. 10, issue. 4, pp. 398–407, 2011.
[9]L. Liu, N. Mehandjiev, D.-L. Xu, “Context similarity metric for multidimensional service recommendation,” International Journal of Electronic Commerce, vol. 18, issue. 1, pp. 73–104, 2013.
[10]M. K. Condliff, D. D. Lewis, D. Madigan, “Bayesian mixed-effects models for recommender systems,” ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation, 1999.
[11]I. Rish, “An empirical study of the naive bayes classifier,” International Joint Conferences on Artificial Intelligence(IJCAI) Workshop on Empirical Methods in AI, pp. 41–46, 2002.
[12]J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, J. Riedl, Grouplens, “Applying collaborative filtering to usenet news,” Communications of the ACM, vol. 40, issue. 3, pp. 77–87, 1997.
[13]J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, vol. 22, issue. 1, pp. 5–53, 2004.
[14]A. Pucci, M. Gori, M. Maggini, “A random-walk based scoring algorithm applied to recommender engines,” Lecture Notes in Computer Science – Advances in Web Mining and Web Usage Analysis, vol. 4811, pp. 127–146, 2007.
[15]N. N. Liu, M. Zhao, Q. Yang, “Probabilistic latent preference analysis for collaborative filtering,” ACM Conference on Information and Knowledge Management, Hong Kong, China, pp. 759–766, 2009.
[16]K.-J. Kim, H. Ahn, “Collaborative filtering with a user-item matrix reduction technique,” International Journal of Electronic Commerce, vol. 16, issue. 1, pp. 107–128, 2011.
[17]C. Porcel, A. Tejeda-Lorente, M. Martinez, E. Herrera-Viedma, “A hybrid recommender system for the selective dissemination of research resources in a technology transfer office,” Information Sciences, vol. 184, issue.1, pp. 1–19, 2012.
[18]G. Guo, J. Zhang, D. Thalmann, N. Yorke-Smith, “Leveraging prior ratings for recommender systems in e-commerce,” Electronic Commerce Research and Applications, vol. 13, issue.6, pp. 440–455, 2014.
[19]X. Qian, H. Feng, G. Zhao, T. Mei, “Personalized recommendation combining user interest and social circle,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, issue. 7, pp. 1763–1777, 2014.
[20]B. M. Sarwar, G. Karypis, J. A. Konstan, J. T. Riedl, “Application of dimensionality reduction in recommender system – a case study,” ACM WEBKDD workshop, 2000.
[21]B. M. Sarwar, G. Karypis, J. Konstan, J. Riedl, “Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering,” 5th International Conference on Computer and Information Technology, 2002.
[22]Q. Ba, X. Li, Z. Bai, “Clustering collaborative filtering recommendation system based on SVD algorithm,” IEEE International Conference on Software Engineering and Service Science, pp. 963–967, 2013.
[23]Z. Gy¨ongyi, H. Garcia-Molina, J. Pedersen, “Combating web spam with trustrank,” 30th International Conference on Very Large Data Bases (VLDB), Morgan Kaufmann, pp. 576–587, 2004.
[24]N. Slonim, N. Tishby, “The power of word clusters for text classification,” 23rd European Colloquium on Information Retrieval Research(ECIR-01), 2001.
[25]R. Bekkerman, R. El-Yaniv, N. Tishby, Y. Winter, “Distributional word clusters versus words for text categorization,” The Journal of Machine Learning Research, vol. 3, pp. 1183–1208, 2003.
[26]H. Li, T. Jiang, K. Zhang, “Efficient and robust feature extraction by maximum margin criterion,” IEEE Transactions on Neural Networks, vol. 17, issue. 1, pp. 157–165, 2006.
[27]J. Yan, B. Zhang, N. Liu, S. Yan, Q. Cheng, W. Fan, Q. Yang, W. Xi, Z. Chen, “Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, issue. 3, pp. 320–333, 2006.
[28]J.-Y. Jiang, R.-J. Liou, S.-J. Lee, “A fuzzy self-constructing feature clustering algorithm for text classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 23, issue. 3, pp. 335–349, 2011.
[29]S.-J. Lee, C.-S. Ouyang, “A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning,” IEEE Transactions on Fuzzy Systems, vol. 11, issue. 3, pp. 341–353, 2003.
[30]M. Sarwat, J. J. Levandoski, A. Eldawy, M. F. Mokbel, “Lars*: An efficient and scalable location-aware recommender system,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, issue. 6, pp. 1384–1399, 2014.
[31]V. S. Tseng, C.-P. Kao, “A novel similarity-based fuzzy clustering algorithm by integrating PCM and mountain method,” IEEE Transactions on Fuzzy Systems, vol. 15, issue. 6, pp. 1188–1196, 2007.
[32]Z. Zhang, H. Cheng, S. Zhang, W. Chen, Q. Fang, “Clustering aggregation based on genetic algorithm for documents clustering,” IEEE Congress on Evolutionary Computation, pp. 3156–3161, 2008.
[33]J. MacQueen, “Some methods for classification and analysis of multivariate observations,” 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, vol. 1, no. 14, pp. 281–297, 1967.
[34]Z. Huang, W. Chung, H. Chen, “A graph model for e-commerce recommender systems,” Journal of the American Society for Information Science and Technology, vol. 55, issue. 3, pp. 259–274, 2004.
[35]D. Harel and Y. Koren, “On clustering using random walks,” Conference on the Foundations of Software Technology and Theoretical Computer Science, pp. 18-41, 2001.
[36]H. Yildirim, M. S. Krishnamoorthy, “A random walk method for alleviating the sparsity problem in collaborative filtering,” ACM conference on Recommender systems, pp. 131–138, 2008.
[37]Movielens datasets,
http://www.grouplens.org/node/73#attachments, accessed: 2007.
[38]R. Salakhutdinov, A. Mnih, “Probabilistic matrix factorization,” Neural Information Processing Systems 20 (NIPS’07), pp. 1257–1264, 2008.
[39]M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, “Social contextual recommendation,” 21st ACM international conference on Information and knowledge management, pp. 45–54, 2012.
[40]D. M. Blei, A. Y. Ng, M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.
[41]F. Fouss, A. Pirotte, J.-M. Renders, M. Saerens, “Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, issue. 3, pp. 355–369, 2007.
[42]A. N. Nikolakopoulos, M. Kouneli, J. Garofalakis, “A novel hierarchical approach to ranking-based collaborative filtering,” Communications in Computer and Information Science, Engineering Applications of Neural Networks, pp. 50–59, 2013.
[43]H. Cui, M. Zhu, “Collaboration filtering recommendation optimization with user implicit feedback,” Journal of Computational Information Systems, vol. 10, issue. 14, pp. 5855–5862, 2014.
[44]D. Wu, G. Zhang, J. Lu, “A fuzzy preference tree-based recommender system for personalized business-to-business e-services,” IEEE Transactions on Fuzzy Systems, vol. 23, issue. 1, pp. 29–43, 2015.