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Ranking Across Different Content Types: The Robust Beauty of Multinomial
  Blending

Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending

ACM Conference on Recommender Systems (RecSys), 2024
17 August 2024
Jan Malte Lichtenberg
Giuseppe Di Benedetto
M. Ruffini
ArXiv (abs)PDFHTML

Papers citing "Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending"

3 / 3 papers shown
A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives
A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives
Kuan Zou
Aixin Sun
OffRL
158
2
0
07 Sep 2025
Calibrated Recommendations with Contextual Bandits
Calibrated Recommendations with Contextual Bandits
Diego Feijer
Himan Abdollahpouri
Sanket Gupta
Alexander Clare
Yuxiao Wen
Todd Wasson
Maria Dimakopoulou
Zahra Nazari
Kyle Kretschman
M. Lalmas
161
0
0
05 Sep 2025
DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation
DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation
Jan Malte Lichtenberg
Antonio De Candia
M. Ruffini
97
0
0
25 Aug 2025
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