Abstract

With rapid development of e-commerce, how to better understand users’ needs to provide more satisfying personalized services has become a crucial issue. To overcome the problem, this study presents a novel approach for personalized product recommendation based on Fuzzy C-Means (FCM) clustering. Firstly, the traditional FCM clustering algorithm is improved by membership adjustment and density function, in order to address the issues that the number of clusters is difficult to determine and the convergence of objective function is slow. Then, the personal preferences are divided into different groups, one of which the users have the similar tendencies in. The association rules of user preferences are mined for each group and the personalized knowledge base is established. After that, the recommendation can be generated by knowledge base and historical logs. A case study is illustrated by the proposed approach and the results show that the method of personalized product recommendation is reasonable and efficient with high performance.

Keywords:

E-commerce, fuzzy clustering, fuzzy c-means, personalized product recommendation,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.