Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining
Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, es...
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: | |
Online Access: | https://www.mdpi.com/2076-3417/9/9/1894 |
Similar Items
-
Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
by: Zhi-Peng Zhang, et al.
Published: (2019-05-01) -
An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem
by: Mahamudul Hasan, et al.
Published: (2019-07-01) -
Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
by: T. Nguyen, et al.
Published: (2018-12-01) -
Co-Displayed Items Aware List Recommendation
by: Junshuai Song, et al.
Published: (2020-01-01) -
FeatureMF: An Item Feature Enriched Matrix Factorization Model for Item Recommendation
by: Haiyang Zhang, et al.
Published: (2021-01-01)