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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(4): 377-381 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.4.715

Solution for Ordered Weighted Averaging Operator for Making in the Interaction multi-Criteria Decision in User-Based Collaborative Filtering Recommender System

Tri Minh Huynh, Vu The Tran, Hung Huu Huynh, and Hiep Xuan Huynh
Abstract—In the recommender system, the most important is the decision-making solution to consulte for user. Depending on the type and size of data stored, decision-making will always be improved to produce the best possible result. The main task in implementing the model is to use methods to find the most valuable product or service for the user. In this paper, we propose a new approach to building a multi-user based collaborative filtering model using the interaction multi-criteria decision with ordered weighted averaging operator. This model demonstrates the synergy and interplay between user criteria for decision making. The model was evaluated through experimentation with the multirecsys tool on three datasets: MovieLense 100K, MSWeb and Jester5k. The experiment illustrated the model comparison with some other interactive multi-criteria counseling methods that have been reserched on both sparse datasets and thick datasets. In addition, the model is compared and evaluated with item-base collaborative filtering model using the interaction multi-criteria decision with ordered weighted averaging operator on both types of datasets. Consultancy results of the proposed model are quite effective compared to some traditional consulting models and some models with other operator. This counseling model can be applied well in a variety of contexts, especially in the case of sparse data that will result in improved counseling. In addition, with the above method, the user-base model is always more efficient than item-base on all datasets.

Index Terms—User-base, item-base, collaborative filtering recommender system, the interaction multi-criteria decision, ordered weighted averaging operator.

Tri Minh Huynh is with Kien Giang University, Viet Nam (e-mail: hmtri@vnkgu.edu.vn).
Vu The Tran and Hung Huu Huynh are with University of Science and Technology, Da Nang University, Viet Nam (e-mail: ttvu@dut.udn.vn, hhhung@dut.udn.vn).
Hiep Xuan Huynh is with Can Tho University, Viet Nam (e-mail: hxhiep@ctu.edu.vn).

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Cite: Solution for Ordered Weighted Averaging Operator for Making in the Interaction multi-Criteria Decision in User-Based Collaborative Filtering Recommender System, "Tri Minh Huynh, Vu The Tran, Hung Huu Huynh, and Hiep Xuan Huynh," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 377-381, 2018.

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