Cargando…

An improved collaborative filtering method based on similarity

The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologie...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Junmei, Fengs, Xiaoyi, Zhang, Ning, Peng, Jinye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152957/
https://www.ncbi.nlm.nih.gov/pubmed/30248112
http://dx.doi.org/10.1371/journal.pone.0204003
_version_ 1783357448329887744
author Feng, Junmei
Fengs, Xiaoyi
Zhang, Ning
Peng, Jinye
author_facet Feng, Junmei
Fengs, Xiaoyi
Zhang, Ning
Peng, Jinye
author_sort Feng, Junmei
collection PubMed
description The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data.
format Online
Article
Text
id pubmed-6152957
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-61529572018-10-19 An improved collaborative filtering method based on similarity Feng, Junmei Fengs, Xiaoyi Zhang, Ning Peng, Jinye PLoS One Research Article The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue. In order to improve the accuracy and quality of recommendations, we proposed an improved similarity model, which takes three impact factors of similarity into account to minimize the deviation of similarity calculation. Compared with the traditional similarity measure, the advantages of our proposed model are that it makes full use of rating data and solves the problem of co-rated items. To validate the efficiency of the proposed algorithm, experiments were performed on four datasets. Results show that the proposed method can effectively improve the preferences of the recommender system and it is suitable for the sparsity data. Public Library of Science 2018-09-24 /pmc/articles/PMC6152957/ /pubmed/30248112 http://dx.doi.org/10.1371/journal.pone.0204003 Text en © 2018 Feng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Feng, Junmei
Fengs, Xiaoyi
Zhang, Ning
Peng, Jinye
An improved collaborative filtering method based on similarity
title An improved collaborative filtering method based on similarity
title_full An improved collaborative filtering method based on similarity
title_fullStr An improved collaborative filtering method based on similarity
title_full_unstemmed An improved collaborative filtering method based on similarity
title_short An improved collaborative filtering method based on similarity
title_sort improved collaborative filtering method based on similarity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6152957/
https://www.ncbi.nlm.nih.gov/pubmed/30248112
http://dx.doi.org/10.1371/journal.pone.0204003
work_keys_str_mv AT fengjunmei animprovedcollaborativefilteringmethodbasedonsimilarity
AT fengsxiaoyi animprovedcollaborativefilteringmethodbasedonsimilarity
AT zhangning animprovedcollaborativefilteringmethodbasedonsimilarity
AT pengjinye animprovedcollaborativefilteringmethodbasedonsimilarity
AT fengjunmei improvedcollaborativefilteringmethodbasedonsimilarity
AT fengsxiaoyi improvedcollaborativefilteringmethodbasedonsimilarity
AT zhangning improvedcollaborativefilteringmethodbasedonsimilarity
AT pengjinye improvedcollaborativefilteringmethodbasedonsimilarity