Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

Jingyi Li, Shaowu Bao
Copyright: © 2024 |Pages: 15
DOI: 10.4018/IJITWE.335123
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Abstract

In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.
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Xia (2016) supposed that if two users share a similar interest, it is very possible for them to select similar products. Thus, Xia designed an e-commerce product recommendation algorithm based on a CF model to compute the recommendation score. Ishida et al. (2017) contributed by showing the review to influence the user’s decision-making process. This unique system can generate a recommendation sentence aligned with the user’s preferences from a user profile that contains the product tag data.

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