# Learning to Crawl

arXiv: https://arxiv.org/abs/1905.12781 · HTML: https://mlsanity.com/paper/1905.12781/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 5 of 5)

- [SMART: Self-learning Meta-strategy Agent for Reasoning Tasks](https://mlsanity.com/paper/2410.16128/) (2024)
- [Look back, look around: a systematic analysis of effective predictors for new outlinks in focused Web crawling](https://mlsanity.com/paper/2111.05062/) (2021)
- [The Construction of Reality in an AI: A Review](https://mlsanity.com/paper/2302.05448/) (2023)
- [A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals](https://mlsanity.com/paper/2502.02430/) (2025)
- [Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems](https://mlsanity.com/paper/2610.19179/) (2026)

## Abstract (arXiv)

Web crawling is the problem of keeping a cache of webpages fresh, i.e., having the most recent copy available when a page is requested. This problem is usually coupled with the natural restriction that the bandwidth available to the web crawler is limited. The corresponding optimization problem was solved optimally by Azar et al. [2018] under the assumption that, for each webpage, both the elapsed time between two changes and the elapsed time between two requests follow a Poisson distribution with known parameters. In this paper, we study the same control problem but under the assumption that the change rates are unknown a priori, and thus we need to estimate them in an online fashion using only partial observations (i.e., single-bit signals indicating whether the page has changed since the last refresh). As a point of departure, we characterise the conditions under which one can solve the problem with such partial observability. Next, we propose a practical estimator and compute confidence intervals for it in terms of the elapsed time between the observations. Finally, we show that the explore-and-commit algorithm achieves an O(√T) regret with a carefully chosen exploration horizon. Our simulation study shows that our online policy scales well and achieves close to optimal performance for a wide range of the parameters.