Matrix Factorization Techniques for Recommender Systems

By P. Seemann. 2014

1,308 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.

Towards a More Reliable Privacy-preserving Recommender System (2017, methodology): “We choose Matrix Factorization (MF) [17] to realize SDCF, and develop Secure Distributed Matrix Factorization (SDMF), for the following reasons.”
Video Relationship Reasoning Using Gated Spatio-Temporal Energy Graph (2019, methodology): “Thanks to the recent advances in graphical models [33, 34, 21], deep neural networks [22, 35], and tensor factorization [14], our workaround is to parametrize and approximate f as f θ with learnable parameters θ as follows:”
Exploring temporal effects for location recommendation on location-based social networks (2013, methodology): “To solve largescale recommendation problems, matrix factorization is state-ofthe-art technology that has been proven to be successful in the Netflix Competition [11, 12], and is being used for item recommendation and trust prediction on product review sites like Epinions and Ciao for research purpos”
A Meta-Learning Perspective on Cold-Start Recommendations for Items (2017, background): “Matrix factorization (MF) is the most popular instantiation of latent factor models and has been used for large scale recommendations of products [19], movies [15]) and news [7].”
Multi-relational matrix factorization using bayesian personalized ranking for social network data (2012, methodology): “Matrix factorization models [4] represent a matrix as the product of two lower-rank matrices.”