Collaborative filtering algorithm based on multi-factors
by Chonghuan Xu; Jie Wang; Jiangjun Yuan
International Journal of Computing Science and Mathematics (IJCSM), Vol. 11, No. 1, 2020

Abstract: Recommender systems are widely used to provide e-commerce users appropriate items and have emerged in response to the problem of information overload. Collaborative filtering (CF) is one of the most successful recommender methods which recommend items to a given user based on the opinions of the similar users. However, the existing CF methods lack the consideration of factors such as time and geo-location. In this paper, we take into account many influencing factors including time and geo-location in the process of similarity computation. The simulation results on two real-world data sets show that our algorithm achieves superior performance to existing methods.

Online publication date: Mon, 02-Mar-2020

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