EBCR: Empirical Bayes concordance ratio method to improve similarity measurement in memory-based collaborative filtering
Recommender systems aim to provide users with a selection of items, based on predicting their preferences for items they have not yet rated, thus helping them filter out irrelevant ones from a large product catalogue. Collaborative filtering is a widely used mechanism to predict a particular user’s...
Main Authors: | Yu Du, Nicolas Sutton-Charani, Sylvie Ranwez, Vincent Ranwez |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2021-01-01
|
Series: | PLoS ONE |
Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351953/?tool=EBI |
Similar Items
-
EBCR: Empirical Bayes concordance ratio method to improve similarity measurement in memory-based collaborative filtering.
by: Yu Du, et al.
Published: (2021-01-01) -
Two Simple and Efficient Algorithms to Compute the SP-Score Objective Function of a Multiple Sequence Alignment.
by: Vincent Ranwez
Published: (2016-01-01) -
User centered and ontology based information retrieval system for life sciences
by: Sy Mohameth-François, et al.
Published: (2012-01-01) -
Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison
by: Yong Zheng
Published: (2022-01-01) -
Enseignement et recherche - Au cœur de #DigitAg, l'Institut Convergences Agriculture Numérique, une Graduate School innovante
by: V. RANWEZ, et al.
Published: (2019-08-01)