Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time beca...

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Bibliographic Details
Main Authors: Zhi-Peng Zhang, Yasuo Kudo, Tetsuya Murai, Yong-Gong Ren
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Applied Sciences
Subjects:
recommender system
item-based collaborative filtering
predictive accuracy
classification accuracy
item-variance weighting
Online Access:https://www.mdpi.com/2076-3417/9/9/1928

Internet

https://www.mdpi.com/2076-3417/9/9/1928

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