High Accuracy and Low Regret for User-Cold-Start Using Latent Bandits
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Abstract (arXiv)
We develop a novel latent-bandit algorithm for tackling the cold-start problem for new users joining a recommender system. This new algorithm significantly outperforms the state of the art, simultaneously achieving both higher accuracy and lower regret.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 12 May 2023.
Builds on
3 of 9 references.
References · 3
- 2014Learning to Optimize via Information-Directed Samplingcited by 168
- 2020Latent Bandits Revisitedcited by 44
- 2015BLC: Private Matrix Factorization Recommenders via Automatic Group Learningcited by 1
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