Personalized Web Search
This addresses the need for better user experience in search engines, but it is incremental as it builds on existing multi-armed bandit methods.
The paper tackled the problem of personalizing web search by modeling long-term and short-term user behavior using a multi-armed bandit algorithm, resulting in improved performance over default ranking and outperforming other bandit algorithms.
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.