A survey of active learning in collaborative filtering recommender systems

By Mehdi Elahi, Francesco Ricci, Neil Rubens. 2016

299 citations recorded by the source corpus.

Subfields: Computer Science Applications, 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 Science Applications

1. Jie Lu
2. Qian Zhang
3. Yaochu Jin
4. Mehdi Elahi

In Information Systems

1. Hamed Zamani
2. Philip Bachman
3. Markus Schedl
4. Jie Lu
5. Ching-Wei Chen
6. Alessandro Sordoni
7. Qian Zhang
8. Yaochu Jin

In Management Science and Operations Research

1. Philip Bachman
2. Markus Schedl
3. Jie Lu
4. Ching-Wei Chen
5. Alessandro Sordoni
6. Qian Zhang
7. Yaochu Jin
8. Hamed Zamani

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).”
• Current challenges and visions in music recommender systems research (2017, background): “Active learning addresses this problem at its origin by identifying and eliciting (high quality) data that can represent the preferences of users be‹er than by what they provide themselves [43, 112].”
• Artificial intelligence in recommender systems (2020, background): “Active learning has been introduced to help recommender systems select the most representative items and deliver them to users to rate [130].”