WCSE

Collaborative Filtering Recommendation Algorithm Based on User Evaluation and Potential Semantic Analysis

Abstract— in order to solve the problem of unitary similarity and inexact data in traditional collaborative filtering algorithm, a collaborative filtering recommendation algorithm combining user evaluation and potential semantic analysis(CFRAEPSA) was proposed .The algorithm consists of two key parts. First, the TF-IDF idea is added into the weight calculation of labels, and the exponential decay function and time window are used to capture the changes of users' interests .Secondly ,it combines the potential semantic analysis algorithm to ensure the relevance of the recommended products .The experimental results show that the proposed algorithm is better than the traditional recommendation method in every evaluation index, and can ensure that the proposed algorithm can effectively improve the recommendation accuracy

Index Terms—Collaborative Filtering, TF – IDF, Exponential Attenuation Function, Latent Semantic Analysis, Item Similarity

Jinlong Chen, Tao Zhang
Guangxi Key Laboratory of Cryptography and Information Security , Guilin University of Electronic Technology, CHINA
Guangxi Key Laboratory of Trusted Software , Guilin University of Electronic Technology, CHINA

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Cite:Jinlong Chen, Tao Zhang, "Collaborative Filtering Recommendation Algorithm Based on User Evaluation and Potential Semantic Analysis " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 252-258, Shanghai, China, 19-21 June, 2020.