New ranking bandit algorithms improve carousel interface recommendations

Researchers have developed new algorithms for ranking bandits in carousel interfaces, which allow recommender systems to observe user browsing depth. The proposed methods, including OD-TS, PBM-TS, and PBM-UCB, update item statistics based on observed exposures rather than treating examination as latent. Experiments show that OD-TS and PBM-TS achieve similar final mean regret, while the new methods generally outperform PBM-UCB in both synthetic and real-world scenarios. AI

RANK_REASON The cluster contains a research paper detailing new algorithms for ranking bandits. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Hugging Face
• OD-TS
• PBM-TS
• PBM-UCB
• RecGaze
• Thompson sampling
• Tsuyoshi Nakamura
• University of California, Berkeley

• paper
• other

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