Lightweight and personalised e-commerce recommendation based on collaborative filtering and LSH
by Dejuan Li; James A. Esquivel
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 45, No. 2, 2024

Abstract: Nowadays, e-commerce has become one of the most popular shopping ways for worldwide customers especially after the outbreak of COVID-19 worldwide. To aid the scientific shopping decision-makings of customers, collaborative filtering is often used to discover similar customers as well as their common shopping preferences. However, traditional collaborative filtering methods often need to read massive shopping records of customers, which usually consumes much time for discovering the customer preferences and consequently, leads to a slow response and decreases customers' shopping quality of experiences. Moreover, traditional collaborative filtering methods cannot always guarantee to discover similar customers as well as their common shopping preferences especially when different customers share few commonly-bought commodities. Motivated by the above two limitations, locality-sensitive hashing used widely in information retrieval domain is recruited in this paper to aid e-commerce platforms to make accurate and scientific shopping decisions for the customers of the platforms. The advantage of our solution is that it can help to improve the response efficiency of e-commerce platforms and provide lightweight and personalised e-commerce recommendation strategies especially when the shopping records of customers are both massive and sparse. We prove the innovations of our algorithm with multiple sets of experiments.

Online publication date: Thu, 22-Feb-2024

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