GPUMF: A GPU-Enpowered Collaborative Filtering Algorithm through Matrix Factorization

Li, Feng; Zhang, Shucheng; Ye, Yunming; Han, Xishuang

doi:10.1109/icss.2015.42

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Wang et al 10 introduced the concept of volatility in the ant colony algorithm and improved the algorithm for mining user browsing preference paths based on the consideration of time variables and distance variables of items. Li et al 11 proposed an iterative least squares‐based CF algorithm with regularization based on the traditional MF model (singular value decomposition, SVD). For the large‐scale website data in the recommendation system, Zhang 12 proposed a distributed MF algorithm based on the cloud platform, which can complete the CF process in the recommendation system in a distributed manner.…”

Section: Related Workmentioning

confidence: 99%

Research on e‐commerce recommendation system based on matrix factorization algorithm

Wang

Qin

Shi

et al. 2023

Concurrency and Computation

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SummaryThe development of the internet has brought great convenience to people's travel and shopping. More and more people choose to shop online. As e‐commerce continues to grow in scale, the number and variety of products are also growing rapidly, which results in customers taking a lot of time to find the products they want to buy. This problem prevents people from using the Internet quickly and efficiently. In order to solve these problems, personalized recommendation system comes into being. It can directly predict the content that users may be interested in based on their historical behavior, and make personalized recommendations for them in the massive data. Based on the idea of collaborative filtering, this paper adopts matrix factorization method to analyze the sales records of an e‐commerce platform, and analyzes the potential preferences of 686 customers, and gives the top five personalized recommended products StockCode of users.

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Section: Related Workmentioning

confidence: 99%