Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' pre...

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Bibliographic Details
Main Authors: Charinya Wangwatcharakul, Sartra Wongthanavasu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Recommender systems
user preference drift
topic evolution
dynamic collaborative filtering
data sparsity
Online Access:https://ieeexplore.ieee.org/document/9090188/

Internet

https://ieeexplore.ieee.org/document/9090188/

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