An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculat...

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Bibliographic Details
Main Authors: Mahamudul Hasan, Falguni Roy
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Big Data and Cognitive Computing
Subjects:
item-based collaborative filtering
cold-start problem
genre
asymmetric correlation
transitive inference
Online Access:https://www.mdpi.com/2504-2289/3/3/39

Internet

https://www.mdpi.com/2504-2289/3/3/39

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