Facing the cold start problem in recommender systems

By Blerina Lika, Kostas Kolomvatsos, Stathes Hadjiefthymiades. 2014

692 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Adam Trischler
2. Alessandro Sordoni
3. Philip Bachman
4. Jianfeng Gao
5. Takao Murakami
6. Yoshua Bengio
7. Mohit Bansal
8. Percy Liang

In Information Systems

1. Philip Bachman
2. Maurizio Morisio
3. Erion Çano
4. Alessandro Sordoni
5. Takao Murakami
6. Adam Trischler
7. Hamed Zamani
8. W. Bruce Croft

In Management Science and Operations Research

1. Philip Bachman
2. Alessandro Sordoni
3. Takao Murakami
4. Adam Trischler

Citations in the corpus

• Learning Algorithms for Active Learning (2017, background): “The cold-start problem is ubiquitous in recommendation systems (Aggarwal, 2016; Lika et al., 2014; Harpale & Yang, 2008; Sun et al., 2013b; Elahi et al., 2016).”
• Hybrid recommender systems: A systematic literature review (2017, background): “Cold-start This problem is heavily addressed in the literature [20,21] and has to do with recom-”
• Model Inversion Attacks for Prediction Systems: Without Knowledge of Non-Sensitive Attributes (2017, methodology): “ML models and is used for resolving the cold start problem of recommendation systems [24].”