Research on collaborative filtering based on user interest in the higher vocational e-commerce website development

doi:10.1088/1742-6596/1982/1/012171

Paper

Research on collaborative filtering based on user interest in the higher vocational e-commerce website development

Published Jul 1, 2021 · Songjie Gong

Journal of Physics: Conference Series

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Abstract

E-commerce Recommender Systems suggest useful and interesting products to customers in order to increase user satisfaction and online conversion rates. They typically exploit explicit or implicit user feedback such as ratings, buying records or clickstream data and apply statistical methods to derive recommendations. Collaborative filtering is one of the most widely used approaches in recommender systems. However, the traditional collaborative filtering methods compute users’ similarities in the dimension of products, and they do not take the influence of neighbor users into consideration when computing such similarities. This paper focuses on explicitly formulated user requirements as the sole type of user feedback. The new collaborative filtering method based on user interests’ transmission. This method computes customers’ similarities in the dimension of interests, and takes the interests transmission between different customers into reflection. This method not only can cope with cold start problem and data sparse problem, but also have prediction precision.

Study Snapshot

Key takeawayThis paper presents a new collaborative filtering method for e-commerce recommender systems, which computes user similarities in interests and considers the influence of neighbor users, improving prediction precision and user satisfaction.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Research on collaborative filtering based on user interest in the higher vocational e-commerce website development

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