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 Item-based collaborative filtering in the multi-criteria recommender system with ordered weighted averaging operator on sparse datasets
Tác giả hoặc Nhóm tác giả: Tri Minh Huynh, Vu The Tran, Hung Huu Huynh, Hiep Xuan Huynh
Nơi đăng: The International Conference on Information and Communication Technology and Digital Convergence Business (ICIDB-2018); Số: 1;Từ->đến trang: accepted;Năm: 2018
Lĩnh vực: Chưa xác định; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
At present, the demand for consulting for users is increasing with the information is very diverse. Recommender system is one of the research goals that are of great interest to scientists. Many recommender methods are designed to find the most valuable products or services that suggest the user to be consulted best. Selecting a suitable solution for the advisory on data storage will address the requirements of the users. In this paper, we propose a new approach to building multi-criteria recommender model that interacts based on items-based collaborative filtering using the ordered weighted average operator on sparse datasets. This model demonstrates the coherence and impact of user criteria in decision-making. The model was evaluated empirically on the multirecsys tool on three datasets: MovieLense 100K, MSWeb and Jester5k. The experiment also illustrates the comparison with some other applied research methods. Consultancy results of the proposed model are quite effective compared to some traditional consulting models. This counseling model can be applied well in a variety of contexts. Especially in the case of sparse data, the counseling results of the proposed model are seem always better than the exiting models.
ABSTRACT
At present, the demand for consulting for users is increasing with the information is very diverse. Recommender system is one of the research goals that are of great interest to scientists. Many recommender methods are designed to find the most valuable products or services that suggest the user to be consulted best. Selecting a suitable solution for the advisory on data storage will address the requirements of the users. In this paper, we propose a new approach to building multi-criteria recommender model that interacts based on items-based collaborative filtering using the ordered weighted average operator on sparse datasets. This model demonstrates the coherence and impact of user criteria in decision-making. The model was evaluated empirically on the multirecsys tool on three datasets: MovieLense 100K, MSWeb and Jester5k. The experiment also illustrates the comparison with some other applied research methods. Consultancy results of the proposed model are quite effective compared to some traditional consulting models. This counseling model can be applied well in a variety of contexts. Especially in the case of sparse data, the counseling results of the proposed model are seem always better than the exiting models.
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