Comparison of collaborative filtering algorithms

By Fidel Cacheda, Victor Carneiro, Diego Fernández, Vreixo Formoso. ACM Transactions on the Web, 2011

323 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f0e0f0c0117d8d9304be5fcb1c064d402d5c44b1

How researchers see this paper

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

In Information Systems

1. Yue Shi
2. Jessica Clark
3. Martha A. Larson
4. Alan Hanjalic
5. Foster J. Provost
6. Harald Steck
7. Charu C. Aggarwal
8. Marius Kaminskas

In Marketing

1. Jessica Clark
2. Yue Shi
3. Martha A. Larson
4. Alan Hanjalic
5. Foster J. Provost

In Sociology and Political Science

1. Yue Shi
2. Martha A. Larson
3. Alan Hanjalic
4. Foster J. Provost
5. Jessica Clark

Citations in the corpus

• Collaborative Filtering beyond the User-Item Matrix (2014, background): “Another category of surveys includes experimental analysis of different recommendation algorithms, as well as their characteristics and usefulness with respect to different recommendation scenarios [Breese et al. 1998; Cacheda et al. 2011; Cremonesi et al. 2010; Herlocker et al. 2004].”
• Dimensionality Reduction via Matrix Factorization for Predictive Modeling from Large, Sparse Behavioral Data (2015, methodology): “and Margaritis (2007), Paterek (2007), and Cacheda et al. (2011). Perhaps the most famous example is the SVD-based technique used as part of the algorithm that won the Netflix Prize (Koren 2009).”