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).
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.