Latent Contextual Bandits and their Application to Personalized Recommendations for New Users

Abstract

Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.

arXiv comment25th International Joint Conference on Artificial Intelligence (IJCAI 2016)

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Cited by in corpus (9)

• Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms
• The Importance of Causality in Decision Making: A Perspective on Recommender Systems
• Thompson Sampling with a Mixture Prior
• Online learning in bandits with predicted context
• Robust Contextual Bandit via the Capped-${\ell }_2$ norm
• Hawkes Process Multi-armed Bandits for Disaster Search and Rescue
• Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
• The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems
• A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors