Item-based collaborative filtering recommendation model based on similarity matrix of items
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Keywords:
similarity measures
item-based collaborative filtering recommendation system
rating matrix
similarity matrix
integrated similarity matrix
Abstract
In this paper, we propose a method to improve the accuracy of item-based collaborative filtering recommendation model (IBCF) by integrating the similarity matrix based on the information of item attributes into the process of building recommendation model. It is called integrated recommendation model. In the traditional item-based collaborative filtering recommendation model, the recommendation results are built based only on the rating matrix of users for the items. In this integrated recommendation model, the recommendation results are based on two similarity matrices: the similarity matrix based on the rating value of users for items and the similarity matrix based on the information of item attributes. Through experiments on MSWeb dataset, it shows that the results of our recommendation model are more accurate than the results of traditional item-based collaborative filtering recommendation model.
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Published
Jan 31, 2017
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How to Cite
Phan Quoc Nghia*; Dang Hoai Phuong; Huynh Xuan Hiep. “Item-Based Collaborative Filtering Recommendation Model Based on Similarity Matrix of Items”. The University of Danang - Journal of Science and Technology, vol. 1, no. 110, Jan. 2017, pp. 55-58, https://jst-ud.vn/jst-ud/article/view/2288.
