Matrix factorization techniques for recommender systems

By A. Mavridis, Ανδρέας Μαυρίδης. 2017

135 citations recorded by the source corpus.

How researchers see this paper

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

1. Han Zhu
2. Pengye Zhang
3. Guozheng Li
4. Kun Gai
5. Jie He
6. Han Li
7. Junling Liu
8. Y. Zhang

Citations in the corpus

• Is ChatGPT a Good Recommender? A Preliminary Study (2023, methodology): “Some recent advancements in this field include the use of deep learning models[20], and the use of matrix factorization techniques[26], which are effective in dealing with the sparsity problem in recommendation systems.”
• Learning Tree-based Deep Model for Recommender Systems (2018, background): “Models such as matrix factorization (MF) [19, 30] try to decompose pairwise user-item preferences (e.g., ratings) into user and item factors, then recommend to each user its most preferred items.”