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Relevance meets Diversity: A User-Centric Framework for Knowledge
  Exploration through Recommendations

Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations

7 August 2024
Erica Coppolillo
Giuseppe Manco
A. Gionis
ArXivPDFHTML

Papers citing "Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations"

4 / 4 papers shown
Title
MUSS: Multilevel Subset Selection for Relevance and Diversity
Vu Nguyen
Andrey Kan
47
0
0
14 Mar 2025
Algorithmic Drift: A Simulation Framework to Study the Effects of
  Recommender Systems on User Preferences
Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences
Erica Coppolillo
Simone Mungari
E. Ritacco
Francesco Fabbri
Marco Minici
Francesco Bonchi
Giuseppe Manco
16
0
0
24 Sep 2024
Measuring Recommender System Effects with Simulated Users
Measuring Recommender System Effects with Simulated Users
Sirui Yao
Yoni Halpern
Nithum Thain
Xuezhi Wang
Kang Lee
Flavien Prost
Ed H. Chi
Jilin Chen
Alex Beutel
43
49
0
12 Jan 2021
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
161
312
0
30 Oct 2017
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