Collaborative filtering algorithm based on multi-factors
2020
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.
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