Learning to rank for recommender systems

By Alexandros Karatzoglou, Linas Baltrunas, Yue Shi. 2013

154 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, 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 Information Systems

1. Barbara E. Engelhardt
2. Brandon M Stewart
3. Bela Gipp
4. Joeran Beel
5. Corinna Breitinger
6. Allison June-Barlow Chaney
7. Stefan Langer
8. Aminu Da’u

In Management Science and Operations Research

1. Bela Gipp
2. Barbara E. Engelhardt
3. Allison June-Barlow Chaney
4. Brandon M Stewart
5. Stefan Langer

Citations in the corpus

• How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Additionally, limiting our scope to rating prediction systems would omit models that focus on learning rankings [32] or that otherwise produce rankings directly, such as ordering items by popularity.”
• Research-paper recommender systems: a literature survey (2015, background): “For a comprehensive overview of offline evaluations refer to [275], [276].”
• Recommender Systems (2016, unknown): “An excellent tutorial on ranking methods for recommendations may be found in [323].”