Novelty and Diversity in Recommender Systems

By Pablo Castells, Neil J. Hurley, Saul Vargas. Recommender Systems Handbook, 2015

399 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/f4c42b98e436adda98a5907288c007f4b9f603e2

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. Azzurra Ragone
2. Eugenio Di Sciascio
3. Michael Jugovac
4. Mozhgan Karimi
5. Tommaso Di Noia
6. Dietmar Jannach
7. Andrea Maurino

In Management Science and Operations Research

1. Michael Jugovac
2. Mozhgan Karimi
3. Dietmar Jannach
4. Tommaso Di Noia
5. Eugenio Di Sciascio
6. Azzurra Ragone
7. Andrea Maurino

In Marketing

1. Dietmar Jannach
2. Tommaso Di Noia
3. Eugenio Di Sciascio
4. Azzurra Ragone

Citations in the corpus

• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms (2020, neutral): “RankSys ([8]) Java 8 58 259 38 2016 No manual”
• News recommender systems - Survey and roads ahead (2018, background): “Outside the domain of news recommendation, a number of works have been published over the past ten years on the topic of diversifying recommendations [29].”
• Schema-summarization in linked-data-based feature selection for recommender systems (2017, background): “However, a good balance remains between accuracy and diversity thus showing a good trade-off between the two [2].”