Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Optimal Dependence of Performance and Efficiency of Collaborative Filtering on Random Stratified Subsampling

Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time and accuracy of CF is not fully understood, and clear guidelines for selecting optimal or even appropriate subsampling l...

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Bibliographic Details
Main Authors: Samin Poudel, Marwan Bikdash
Format: Article
Language:English
Published: Tsinghua University Press 2022-09-01
Series:Big Data Mining and Analytics
Subjects:
collaborative filtering (cf)
subsampling
training time improvement (tti)
performance loss
recommendation system (rs)
collaborative filtering optimal solutions
rating matrix
Online Access:https://www.sciopen.com/article/10.26599/BDMA.2021.9020032

Internet

https://www.sciopen.com/article/10.26599/BDMA.2021.9020032

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