2015/02/03 by Li Zhou, Zhou, Li
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Algorithms and Data Compression #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.1502.01057
openalex publication_date 2015/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Personalization is important for search engines to improve user experience. Most of the existing work do pure feature engineering and extract a lot of session-style features and then train a ranking model. Here we proposed a novel way to model both long term and short term user behavior using Multi-armed bandit algorithm. Our algorithm can generalize session information across users well, and as an Explore-Exploit style algorithm, it can generalize to new urls and new users well. Experiments show that our algorithm can improve performance over the default ranking and outperforms several popular Multi-armed bandit algorithms.