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MORS@RecSys 2022: Seattle, WA, USA
- Himan Abdollahpouri, Shaghayegh Sahebi, Mehdi Elahi, Masoud Mansoury, Babak Loni, Zahra Nazari, Maria Dimakopoulou:
Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September 2022. CEUR Workshop Proceedings 3268, CEUR-WS.org 2022
Session 1
- Preface.
- Dietmar Jannach:
Multi-Objective Recommendation: Overview and Challenges. - Sinan Seymen, Anna-Lena Sachs, Edward C. Malthouse:
Making Smart Recommendations for Perishable and Stockout Products. - Yiding Ran, Hengchang Hu, Min-Yen Kan:
PM K-LightGCN: Optimizing for Accuracy and Popularity Match in Course Recommendation.
Session 2
- Chunpai Wang, Shaghayegh Sahebi, Peter Brusilovsky:
Proximity-Based Educational Recommendations: A Multi-Objective Framework. - Oleg Lesota, Stefan Brandl, Matthias Wenzel, Alessandro B. Melchiorre, Elisabeth Lex, Navid Rekabsaz, Markus Schedl:
Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization. - Peter Knees, Andres Ferraro, Moritz Hubler:
Bias and Feedback Loops in Music Recommendation: Studies on Record Label Impact. - Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering.
Session 3 (Poster Session)
- Tahereh Arabghalizi, Alexandros Labrinidis:
A Ranked Bandit Approach for Multi-stakeholder Recommender Systems. - Mounir Hafsa, Pamela Wattebled, Julie Jacques, Laetitia Jourdan:
A Multi-Objective E-learning Recommender System at Mandarine Academy. - Renata Pelissari, Paulo S. C. Alencar, Sarah Ben Amor, Leonardo Tomazeli Duarte:
A Systematic Review of the Use of Multiple Criteria Decision Aiding Methods in Recommender Systems: Preliminary Results. - Yan Zhao, Mitchell Goodman, Sameer Kanase, Shenghe Xu, Yannick Kimmel, Brent Payne, Saad Khan, Patricia Grao:
Mitigating Targeting Bias in Content Recommendation with Causal Bandits.
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