Matrix Factorization Techniques For Recommender Systems

By Markus Freitag, Jan-Felix Schwarz. 2011

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1687bc48ff8cfbe8a9e2e31780d8737b249db099

How researchers see this paper

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

1. Hao Wang
2. Ameet Talwalkar
3. Lester W. Mackey
4. Michael I. Jordan
5. Preslav Nakov
6. Fernando Ortega
7. Dit-Yan Yeung
8. Yonatan Belinkov

Citations in the corpus

• Recommender systems survey (2013, methodology): “The reduction methods are based on Matrix Factorization [124,142,143].”
• A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines (2013, background): “Recently, matrix factorization techniques that model both users and items in a joint latent factor space have gained popularity for CF (Salakhutdinov & Mnih, 2008; Koren et al., 2009; Lawrence & Urtasun, 2009).”
• Divide-and-Conquer Matrix Factorization (2011, background): “, [19] and the references therein), link prediction for social networks [15], click prediction for web search [6], video surveillance [2], graphical model selection [4], document modeling [27], and image alignment [33].”