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Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Wei Wang
Copyright: © 2022 |Volume: 24 |Issue: 5 |Pages: 28
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781668453926|DOI: 10.4018/JCIT.306977
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MLA

Wang, Wei. "Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction." JCIT vol.24, no.5 2022: pp.1-28. http://doi.org/10.4018/JCIT.306977

APA

Wang, W. (2022). Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction. Journal of Cases on Information Technology (JCIT), 24(5), 1-28. http://doi.org/10.4018/JCIT.306977

Chicago

Wang, Wei. "Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction," Journal of Cases on Information Technology (JCIT) 24, no.5: 1-28. http://doi.org/10.4018/JCIT.306977

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Abstract

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.