政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/101129

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/101129

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题名:	冷起始使用者的跨領域推薦機制 Cross-Domain Recommendation Mechanism for Cold-Start Users
作者:	徐嘉泰 Hsu, Chia Tai
贡献者:	沈錳坤 Shan, Man Kwan 徐嘉泰 Hsu, Chia Tai
关键词:	推薦系統跨領域推薦系統冷起始使用者偏最小平方法偏最小平方迴歸 recommender system cross-domain recommender system cold-start user partial least squares partial least squares regression
日期:	2016
上传时间:	2016-09-02 00:13:37 (UTC+8)
摘要:	推薦系統在近代的網路購物中十分常見，在多數的電子商務網站皆可見其蹤跡。推薦系統可以幫助使用者在無垠的商品海中挑選適當的商品，節省使用者的時間與精力，同時也能夠提升商品的銷售量，增加商家的獲利。跨領域推薦系統(cross-domain recommender system)是推薦系統的其中一種，對於此方面的研究漸漸興起，跨領域推薦系統主要是希望利用使用者對不同領域物品的的喜好的特性進行推薦。冷起始使用者問題(cold-start user problem)是推薦系統中十分常見的實務問題，在使用者缺乏資訊的情況下，推薦系統難以為這些使用者推薦物品、或是造成推薦的不準確性。目前有一些跨領域推薦系統的研究，但鮮少解決跨領域推薦中的冷起始使用者問題，特別是使用者在目標領域全無資訊的情況下的困境，因此本研究提出解決此問題的方法。本研究旨在提出一個基於相關性分析的協同式過濾(collaborative filtering)跨領域推薦模型，該模型主要解決在多重領域的情況下使用者於目標領域的冷起始問題。本研究使用PLSR(Partial Least Squares Regression)作為整個模型的核心方法，利用PLSR迴歸預測的特性預測使用者跨領域的評分(rating)。此外，過程中結合其他的推薦技術以解決PLSR無法應用於有缺值(missing value)資料的問題。本研究最主要的貢獻，在於解決推薦對象在目標領域冷起始的情況低推薦準確率的問題，實驗也證明本研究所提出的推薦方法有不錯的Mean Absolute Error與Root Mean Squared Error評估數值，特別是在資料較少的情況下還能比其他推薦方法有更佳的推薦效果。 Recommender systems are common in the E-Commerce platforms in recent years. Recommender systems are able to help users find suitable items among large amount of products. It can save users’ time and increase profits of sellers. Cross-domain recommender systems, which related researches are blooming recently, is a sort of recommender systems that recommend items based on users’ different taste across domains. Cold-start users problem is a common practical problem in recommender systems. In the case of scarce information of users, it is difficult for recommender systems to recommend items or even lead to inaccuracy of recommendation results. There are few studies on dealing with cold-start users problem in cross-domain recommender systems, especially in the case of shortage of information. This thesis proposes a collaborative filtering cross-domain recommendation model based on correlation analysis, which mainly solve the cold-start problem that users have no preference information in the target domain in cross-domain situation. The proposed recommendation model is able to predict the rating among multiple domains for cold start users based on the Partial Least Squares Regression (PLSR). The proposed model incorporates other recommendation techniques for dealing with the problem that PLSR cannot use the data with missing values as input. The primary contribution of this research is that the proposed model is able to solve the problem of low recommendation accuracy for cold-start users in target domain. The experiment shows Mean Absolute Error and Root Mean Squared Error of the proposed model are better than the other approaches when data is sparse.
參考文獻:	[1] G. Adomavicius and A. Tuzhilin, 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, 17(6), 734-749, 2005. [2] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, Recommender systems survey. Knowledge-Based Systems, 46, 109-132, 2013. [3] J. S. Breese, D. Heckerman, and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, 1998. [4] I. Cantador, I. Fernández-Tobías, S. Berkovsky, and P. Cremonesi, Cross-domain recommender systems, Springer, 2015. [5] P. Cremonesi, A. Tripodi, and R. Turrin, Cross-domain recommender systems. IEEE 11th International Conference on Data Mining Workshops, 2011. [6] S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman, Indexing by latent semantic analysis. Journal of the American Society For Information Science, 41(6), 391-407, 1990. [7] I. Fernández-Tobías, I. Cantador, M. Kaminskas, and F. Ricci, Cross-domain recommender systems: A survey of the state of the art. Proceedings of the 2nd Spanish Conference on Information Retrieval, 2012. [8] I. Jolliffe, Principal component analysis. Wiley Online Library, 2002. [9] Y. Koren, R. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37, 2009. [10] D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 2001. [11] D. Li, N. Dimitrova, M. Li, and I. K. Sethi, Multimedia content processing through cross-modal association. Proceedings of the 11th ACM International Conference on Multimedia, 2003. [12] B. Li, Q. Yang, and X. Xue, Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction. Proceedings of the 21st International Joint Conference on Artifical Intelligence, 2009. [13] R. Manne, Analysis of two partial-least-squares algorithms for multivariate calibration. Chemometrics and Intelligent Laboratory Systems, 2(1), 187-197, 1987. [14] S. Meyffret, E. Guillot, L. Médini, and F. Laforest, RED: a Rich Epinions Dataset for Recommender Systems.2014. [15] N. Mirbakhsh and C. X. Ling, Improving top-N recommendation for cold-start users via cross-domain information. ACM Transactions on Knowledge Discovery from Data, 9(4), 33, 2015. [16] R. J. Mooney and L. Roy, Content-based book recommending using learning for text categorization. Proceedings of the 5th ACM conference on Digital Libraries, 2000. [17] W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang, Transfer learning in collaborative filtering for sparsity reduction. Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010. [18] W. Pan, E. W. Xiang, and Q. Yang, Transfer learning in collaborative filtering with uncertain ratings. Proceedings of the 26th AAAI Conference on Artificial Intelligence, 2012. [19] R. Rosipal and N. Krämer, Overview and recent advances in partial least squares. In Subspace, Latent Structure and Feature Selection, Springer, 34-51, 2006. [20] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 2001. [21] B. Shapira, L. Rokach, and S. Freilikhman, Facebook single and cross domain data for recommendation systems. User Modeling and User-Adapted Interaction, 23(2-3), 211-247, 2013. [22] Y. Shi, M. Larson, and A. Hanjalic, Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In User Modeling, Adaption and Personalization, Springer, 305-316, 2011. [23] A. Tiroshi, S. Berkovsky, M. A. Kaafar, T. Chen, and T. Kuflik, Cross social networks interests predictions based ongraph features. Proceedings of the 7th ACM Conference on Recommender Systems, 2013. [24] Y. Zhang, B. Cao, and D.-Y. Yeung, Multi-domain collaborative filtering. In Proceedings of the 26th Conference Annual Conference on Uncertainty in Artificial Intelligence, 2012 [25] F. Zhuang, P. Luo, H. Xiong, Y. Xiong, Q. He, and Z. Shi, Cross-domain learning from multiple sources: a consensus regularization perspective. IEEE Transactions on Knowledge and Data Engineering, 22(12), 1664-1678, 2010.
描述:	碩士國立政治大學資訊科學學系 101753027
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0101753027
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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