Các phương pháp đánh giá hệ thống gợi ý
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Adomavicius, G. and A. Tuzhilin, 2005. 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.
Adomavicius, G. And Y. Kwon, 2008. Overcoming accuracy-diversity tradeoff in recommender systems: a variance-based approach. In Proceedings of the 18th Workshop on Information Technology and Systems, WITS 2008, Paris, France.
Adomavicius, G. and Y. Kwon, 2010. Improving recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering.
Bradley, 2001. Improving recommendation diversity. In Proceedings of the 12th National Conference in Artificial Intelligence and Cognitive Science. D. O’donoghue, Ed., Maynooth, Ireland, pp. 75–84.
Breese, J.S. and D. Heckerman, 1998. Empirical analysis of predictive algorithms for collaborative filtering. Morgan Kaufmann, pp. 43–52.
Cosley D., et al, 2003. Is seeing believing?: how recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03). ACM, New York, NY, USA, 585-592. DOI=10.1145/642611.642713 http://doi.acm.org/10.1145/642611.642713
Dias et al, 2008. The value of personalised recommender systems to e-business : a case study. Recsys ’08. New York, NY, USA : ACM, pp. 291–294.
Fouss, F. and , 2008. Evaluating performance of recommender systems: an experimental comparison. Web intelligence. IEEE, pp. 735–738.
Herlocker J. L., et al, 2000. Explaining collaborative filtering recommendations. In Proceedings of the 2000 Conference on Computer Supported Cooperative Work, 241–250.
Herlocker J.L et al, 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 5–53.
Hsu, C. and H. Chung, 2004. Mining skewed and sparse transaction data for personalized shopping recommendation. Mach. Learn., vol. 57, no. 1-2, pp. 35–59.
Karypis.g, 2001. Evaluation of item-based top-n recommendation algorithms. Cikm ’01: proceedings of the tenth international conference on information and knowledge management. New york, ny, usa : acm, pp. 247–254.
Koren.Y, 2009. The Bellkor solution to the netflix grand prize. Http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1. 162.2118.
Kozma, L and T. Raiko, 2009. Binary principal component analysis in the netflix collaborative filtering task. In Proceedings of the IEEE Workshop on Machine Learning for Signal Processing.
Mortensen M., 2007. Design and evaluation of a recommender system. Http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.2726
Park, Y. and A. Tuzhilin, 2008. The long tail of recommender systems and how to leverage it. Recsys. ACM, pp. 11–18.
Sarwar B., et al, 2001. Item-based collaborative filtering recommendation algorithms. Proc. 10th international conference on the world wide web, pp. 285–295.
Sarwar, B and G. Karypis, 2000. Analysis of recommendation algorithms for ecommerce. EC ’00. USA : ACM, pp. 158–167.
Schafer J.B., et al, 2007. Collaborative filtering recommender systems. The Adaptive Web, Ser. Lecture Notes in Computer Science, P. Brusilovsky, A. Kobsa, and W. Nejdl, eds. Springer Berlin, heidelberg, vol. 4321, pp. 291–324.
Slaney.M, 2006. Measuring playlist diversity for recommendation systems. In Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia, Ser. AMCMM ’06. New York, Ny, USA: ACM, pp. 77–82.
Takács G., et al, 2007. On the gravity recommendation system. Proc. Of the KDD CUP and Workshop 2007 (KDD 2007), pp. 22–30.
Trần Nguyễn Minh Thư, 2011. Abstraction et règles d’association pour l’amélioration des systèmes de recommendation à partir de données de préférences binaires. Phd thesis.
Yeong, et al, 2005. Mining changes in customer buying behavior for collaborative recommendations. Expert Syst. Appl. 28, 2 (February 2005), 359-369. DOI=10.1016/j.eswa.2004.10.015 http://dx.doi.org/10.1016/j.eswa.2004.10.015.
Yu, C and L. Lakshmanan, 2009. It takes variety to make a world: diversification in recommender systems. In EDBT '09 Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology.
Ziegler C., et al, 2005. Improving recommendation lists through topic diversification. Proceedings of the 14th International Conference on World wide web, ser. WWW ’05. New York, NY, USA : ACM, pp. 22–32.