Collaborative Filtering Recommender Systems

By Michael D. Ekstrand, John Riedl, J. Konstan. 2011

710 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

How researchers see this paper

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

In Computer Networks and Communications

1. John Riedl
2. Yue Shi
3. Yongfeng Zhang
4. J. Konstan
5. Martha A. Larson
6. Alan Hanjalic
7. Xu Chen
8. W. Bruce Croft

In Information Systems

1. Yongfeng Zhang
2. Xu Chen
3. Qingyao Ai
4. Yue Shi
5. John Riedl
6. J. Konstan
7. Xiangnan He
8. W. Bruce Croft

In Management Science and Operations Research

1. Yue Shi
2. John Riedl
3. Martha A. Larson
4. Alan Hanjalic
5. J. Konstan
6. Yongfeng Zhang
7. Xu Chen
8. W. Bruce Croft

Citations in the corpus

• Recommender systems: from algorithms to user experience (2012, background): “Indeed, there have been a number of thorough surveys that focus on the algorithms behind recommenders (Adomavicius and Tuzhlin 2005; Burke 2002; Ekstrand et al. 2011; Herlocker et al. 1999, 2004), and we refer the interested reader to them.”
• Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources (2017, background): “At the very beginning, hybrid recommendation mostly referred to the integration of both content-based [30] and CF-based [14] techniques for recommendation, where the integration can come in various forms such as weighting, switching, mixing, etc, depending on the available content profiles and…”
• Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “We first formally define the problem of CF and then cover the two major categories of traditional CF approaches, the memory-based CF and model-based CF [Adomavicius and Tuzhilin 2005; Ekstrand et al. 2011].”