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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(3): 214-222 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.3.690

Collaborative Filtering Recommendation in the Implication Field

Hoang Nguyen-Tan, Hung Huynh-Huu, and Hiep Huynh-Xuan
Abstract—In the age of information explosion today, the Recommender systems have become increasingly important and popular in supporting human decision-making problems. In the Recommender Systems, 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 this paper, we propose a method new approach in building the collaborative filtering recommender system in the implication field, uses the asymmetry measures to rank and filter the information to improve accurate precision of the traditional recommender systems.

Index Terms—Implication index, implication intensity, implication field, collaborative filtering, implication rule.

Hoang Nguyen-Tan is with the Department of Information and Communications of Dong Thap Province, Viet Nam (e-mail: hoangntdt@gmail.com).
Hung Huynh-Huu is with University of Science and Technology, Da Nang University, Viet Nam (e-mail: hhhung@dut.udn.vn).
Hiep Huynh-Xuan is with Can Tho University, Viet Nam (e-mail: hxhiep@ctu.edu.vn).

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Cite: Hoang Nguyen-Tan, Hung Huynh-Huu, and Hiep Huynh-Xuan, "Collaborative Filtering Recommendation in the Implication Field," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 214-222, 2018.

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