A Fast Bandit Algorithm for Recommendations to Users with Heterogeneous Tastes
- Pushmeet Kohli ,
- Mahyar Salek ,
- Greg Stoddard
AAAI'13 Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence |
Published by AAAI Press
We study recommendation in scenarios where there’s no prior information about the quality of content in the system. We present an online algorithm that continually optimizes recommendation relevance based on behavior of past users. Our method trades weaker theoretical guarantees in asymptotic performance than the state-of-the-art for stronger theoretical guarantees in the online setting. We test our algorithm on real-world data collected from previous recommender systems and show that our algorithm learns faster than existing methods and performs equally well in the long-run.