Hoang Tan Nguyen
ABSTRACT
Collaborative filtering is one of the most popular and effective techniques available today in the recommender system. However, most of them use symmetric similarity measures. Therefore, the default effect and the role of the pair of users are the same, but in practice this may not be true. In addition, they only logically demonstrate the existence of a priority relationship between two users rather than the level of the relationship in practice. In this paper, we propose a new approach for the collaborative filtering based on the variation analysis of the implication index. An asymmetric measure is developed which can be used to rank or filter information based on the variation of the implication index by a counter-example. This measure provides a meaningful recommendation with a certain level of implication. Experimental results shown that the proposed approach can overcome the drawbacks in the traditional recommender systems.
- Adomavicius Gediminas, Tuzhilin Alexander, 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, pp.734--749, 2005. Google Scholar
Digital Library
- Adomavicius Gediminas, Tuzhilin Alexander, Context-aware recommender systems, Springer US, pp. 217--253, 2011.Google Scholar
- Francesco Ricci, Lior Rokach and Bracha Shapira, Introduction to Recommender Systems Handbook, SpringerVerlag and Business Media LLC, pp. 1--35, 2011.Google ScholarCross Ref
- Bin Cao, Qiang Yang, Jian-Tao Sun, Zheng Chen, Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization, Data Mining and Knowledge Discovery, Volume 22, Issue 3, pp.393--418, 2011. Google ScholarDigital Library
- Rahul Katarya, Om Prakash Verma, Effective collaborative movie recommender system using asymmetric user similarity and matrix factorization, The 2016 IEEE International Conference on Computing, Communication and Automation, pp.1--12, 2016.Google Scholar
- Mukund Deshpande, George Karypis, Item-based top-N recommendation algorithms. ACM Transaction on Information Systems 22(1),pp. 143--177, 2004. Google ScholarDigital Library
- Zhi-Lin Zhao Chang-Dong Wang, Jian-Huang Lai AUI&GIV Recommendation with asymmetric user influence and global importance value. Public Library of Science ONE, pp.2016.Google Scholar
- NghiaQuocPhan, KyMinh Nguyen, Hoang Tan Nguyen, Hiep Xuan Huynh, Recommender system based approach combining associationrule and implicative statistical measure, Proceedings of the VIII National Conference onFundamental and Applied IT Research (FAIR' 15); Ha Noi, 2015. (in Vietnamese).Google Scholar
- Lan Phuong Phan, Trang Uyen Tran, Hung Huu Huynh, Hiep Xuan Huynh, the user-based collaborative filtering recommeder system using associaion rules combined implication statistical cohension measure, Proceedings of the IX National Conference onFundamental and Applied IT Research (FAIR'16); Can Tho, 2016, (in Vietnamese).Google Scholar
- Hoang Tan Nguyen, Hung Huu Huynh, Hiep Xuan Huynh, Recommender system based on analysis Implicative statistical user preferences over time, IX International Conference A.S.I. Analyse Statistique Implicative - Statistical Implicative Analysis (ASI9), Franch, pp.493--507, 2017.Google Scholar
- Hoang Tan Nguyen, Hung Huu Huynh, Hiep Xuan Huynh, Recommendation based on the variance of implication index in statistical implication field, Proceedings of the X National Conference onFundamental and Applied IT Research (FAIR'17); Da Nang, 2017. (in Vietnamese) pp.938--950.Google Scholar
- Régis Gras, Pascale Kuntz and Nicolas Greffard, Notion de champ implicatif en analysis statistique implicative, The 8th International Meeting on Statistical Implicative Analysis, Tunisia, pp 1--21, 2015 (in French).Google Scholar
- Régis Gras, Einoshin Suzuki Fabrice Guillet, Filippo Spagnolo (Eds.), Statistical Implicative Analysis, Theory and Application, Springer Verlag Berlin Heidelberg, 2008. Google ScholarDigital Library
- Regis Gras, Raphael Couturier, Spécificités de l'Analyse Statistique Implicative (A.S.I.) par rapport à d'autres mesures de qualité de règles d'association, Quaderni di Ricerca in Didattica - GRIM (ISSN on-line 1592-4424, p.19--57, 2010.Google Scholar
- Dominique Lahanier-Reuter, Didactics of Mathematics and Implicative Statistical Analysis, Statistical Implicative Analysis - Studies in Computational Intelligence, pp 277--298, 2008.Google ScholarCross Ref
- Régis Gras, Dominique Lahanier-Router, Duality between variables space and subjects space of the statistic implicative analysis, Dualite entre espace des variables et espace des sujets en analyse statisticque implicative, The VI International conference, ASI Analyse statistique implicative- Implicative statistical Analysis Caen (ASI6), France, pp 1--28, 2012.Google Scholar
- Régis Gras, Pascale Kuntz, Discovering R-rules with a directed hierarchy, Journal Soft Computing - A Fusion of Foundations, Methodologies and Applications (Volume 10 Issue 5), Springer-Verlag, pp 453--460, 2006. Google ScholarDigital Library
Index Terms
- Collaborative filtering recommendation with threshold value of the equipotential plane in implication field
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