Finding and Applying Typical User Group in Recommender Systems
TAN Chang1, LIU Qi1, WU Le1, MA Hai-Ping2, LONG Bo3
1.School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027 2.iFLYTEK Co.,Ltd., Hefei 230088. 3.China Electronic Technology Group Corporation No.38 Research Institute, Hefei 230088
Abstract Recommender system (RS) provides an effective way to solve the personalized information needs of users. However, with the expansion of the user scale, it is necessary to find some subsets of vast amounts of RS users, and the continuous and in-depth analysis for these user subsets can be used to improve the RS. Therefore, the typical user group (TUG) is defined as a representative subset of the entire users in RS to correctly reflect the preferences of all the users. Then, a weighted typical user group finding algorithm (WTFA) is designed to compare the contributions of the candidate typical users and choose the typical users with higher contribution, so that a TUG is built with high item coverage rate and rating accuracy. A modified TUG-based collaborative filtering(TUG-CF)algorithm is developed to discover the nearest neighbors in TUG. The experimental results on real world dataset show that TUG is better than most rating user group and maximizes diversified user group on item coverage rate and rating accuracy, and TUG-CF has better recommendation results than traditional collaborative filtering methods.
[1] Adomavicius G, Tuzhilin A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749 [2] Kim K J, Ahn H. A Recommender System Using GA K-Means Clustering in an Online Shopping Market. Expert Systems with Applications, 2008, 34(2): 1200-1209 [3] Sarwar B M, Karypis G, Konstan J, et al. Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering. [EB/OL]. [2014-03-20]. Http://glaros.dtc.umn.edu/gkhome/fetch/pagers/clusterICCIT02.pdf [4] Ungar L, Foster D. Clustering Methods for Collaborative Filtering. [EB/OL]. [2014-03-18].http://www.aaai.org/Papers/Workshops/1998/WS-98-08/WS98-08-029.pdf [5] Liu Q, Chen E H, Xiong H, et al. A Cocktail Approach for Travel Package Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2): 278-293 [6] Tan C, Liu Q, Chen E H, et al. Object-oriented Travel Package Recommendation. ACM Transactions on Intelligent Systems and Technology, 2013, 5(3): 43: 1-43:26 [7] Lin H, Bilmes J. A Class of Submodular Functions for Document Summarization // Proc of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, USA, 2011: 510-520 [8] Sipos R, Swaminathan A, Shivaswamy P, et al. Temporal Corpus Summarization Using Submodular Word Coverage // Proc of the 21st ACM International Conference on Information and Knowledge Management. Maui, USA, 2012: 754-763 [9] El-Arini K, Veda G, Shahaf D, et al. Turning Down the Noise in the Blogosphere // Proc of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009: 289-298 [10] Krause A, Guestrin C. Submodularity and Its Applications in Optimized Information Gathering. ACM Transactions on Intelligent Systems and Technology, 2011, 2(4): 389-396 [11] Pennacchiotti M, Silvestri F, Vahabi H, et al. Making Your Interests Follow You on Twitter // Proc of the 21st ACM International Conference on Information and Knowledge Management. Maui, USA, 2012: 165-174 [12] Hammar M, Karlsson R, Nilsson B J. Using Maximum Coverage to Optimize Recommendation Systems in E-Commerce // Proc of the 7th ACM Conference on Recommender Systems. Hong Kong, China, 2013: 265-272 [13] McNee S M, Riedl J, Konstan J A. Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems // Proc of the Extended Abstracts on Human Factors in Computing Systems. Montreal, Canada, 2006: 1097-1101 [14] Zhang M, Hurly N. Evaluating the Diversity of Top-N Recommendations // Proc of the 21st International Conference on Tools with Artificial Intelligence. Newark, USA, 2009: 457-460 [15] Said A, Fields B, Jain B J, et al. User-Centric Evaluation of a K-Furthest Neighbor Collaborative Filtering Recommender Algorithm // Proc of the Conference on Computer Supported Cooperative Work. San Antonio, USA, 2013: 1399-1408 [16] Zhang F G, Xu S H. Research on Recommendation Diversification in Trust Based E-Commerce Recommender Systems. Journal of the China Society for Scientific and Technical Information, 2010, 29(2): 350-355 (in Chinese) (张富国,徐升华.基于信任的电子商务推荐多样性研究.情报学报, 2010, 29(2): 350-355) [17] Cui C Y, Ma J. An Image Tag Recommendation Approach Combining Relevance with Diversity. Chinese Journal of Computers, 2013, 36(3): 654-663 (in Chinese) (崔超然,马 军.一种结合相关性和多样性的图像标签推荐方法.计算机学报, 2013, 36(3): 654-663) [18] Park Y J, Tuzhilin A. The Long Tail of Recommender Systems and How to Leverage It // Proc of the ACM conference on Recommender Systems. Lausanne, Switzerland, 2008: 11-18 [19] Anderson C. The Long Tail. New York, USA: Random House Audiobooks, 2007 [20] Nemhauser G L, Wolsey L A, Fisher M L. An Analysis of Approximations for Maximizing Submodular Set Functions-I. Mathematical Programming, 1978, 14(1): 265-294 [21] Feige U. A Threshold of in n for Approximating Set Cover. Journal of the ACM, 1998, 45(4): 634-652 [22] Liu Q, Xiang B, Chen E H, et al. Influential Seed Items Recommendation // Proc of the 6th ACM Conference on Recommender Systems. Dublin, Ireland, 2012: 245-248 [23] Adomavicius G, Kwon Y O. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Trans on Knowledge and Data Engineering, 2012, 24(5): 896-911 [24] Herlocker J L, Konstan J A, Terveen L G, et al. Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 2004, 22(1): 5-53 [25] Koren Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA, 2008: 624-634 [26] Koren Y, Bell R M, Volinsky C. Matrix Factorization Techniques for Recommender Systems. Computer, 2009, 42(8): 30-37
2015, Vol. 28 
