An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

By Xin Luo, Mengchu Zhou, Yunni Xia, Qingsheng Zhu. IEEE Transactions on Industrial Informatics, 2014

582 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/e5acdb0246b33d33c2a34a4a23faaf21e2f9b924

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Emilia G'omez
2. Giuseppe Rizzo
3. Carlos Castillo
4. Raphael Troncy
5. Enrico Palumbo

In Information Systems

1. Emilia G'omez
2. Giuseppe Rizzo
3. Carlos Castillo
4. Raphael Troncy
5. Dougal Shakespeare
6. Enrico Palumbo
7. Lorenzo Porcaro

Citations in the corpus

• Exploring Artist Gender Bias in Music Recommendation (2020, methodology): “We consider two types of CF algorithms: (1) KNN-based approach: UserKNNAvg [27], and (2) factorisation-based approach: Non-Negative Matrix Factorization (NMF ) [30].”
• entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation (2017, methodology): “As baselines, we use state-of-the-art collaborative filtering algorithms based on Non-Negative Matrix Factorization (NMF) [17], on Singular Value Decomposition [15], ItemKNN with baselines [14] and the Most Popular Items recommendation strategy, which is known to be quite effective on MovieLens due…”