Abstract
As a crucial technology in personalized recommendation, collaborative filtering algorithm has been widely used. Today’s collaborative filtering algorithm is mainly divided into two types, user-based and commodity-based. In order to make it clearer to user-based with regard to the differences and application scenarios of the two collaborative filtering recommendation algorithms from the project, this article analyzes the definitions, basic principles, similarity calculation methods, and the advantages and disadvantages of the two algorithms through the method of comparison. Finally, through analysis and comparison, the current problems of these two algorithms and their respective limitations are proved to provide references for future use and improvement of collaborative filtering algorithms.
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