# Personalized Web Search

arXiv: https://arxiv.org/abs/1502.01057 · HTML: https://mlsanity.com/paper/1502.01057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2015.

## Built on by (most cited first, 1 of 1)

- [Pessimistic Off-Policy Optimization for Learning to Rank](https://mlsanity.com/paper/2206.02593/) (2022)

## Abstract (arXiv)

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.