A survey of serendipity in recommender systems

By Denis Kotkov, Shuaiqiang Wang, J. Veijalainen. Knowledge-Based Systems, 2016

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/cf9c0846c6b1c97b89fb27d5acef6db18fe04039

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. Aminu Da’u
2. Naomie Binti Salim

In Information Systems

1. Aminu Da’u
2. Naomie Binti Salim
3. Iqbal H. Sarker

In Management Science and Operations Research

1. Aminu Da’u
2. Naomie Binti Salim

Citations in the corpus

• Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “Data sparsity is a problem generally affects CF methods which occur as a result of insufficient information in the system (Kotkov et al. 2016).”
• Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity (2018, unknown): “In computer science, there is no consensus on how serendipity should be conceptualized and operationalized, other than unexpectedness (Kotkov et al., 2016).”