Latent based temporal optimization approach for improving the performance of collaborative filtering.

ABSTRACT: Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers' ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers' drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort imp

SUBMITTER: Al-Hadi IAA 

PROVIDER: S-EPMC7924488 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature