Adversarial Bandits Policy for Crawling Commercial Web Content-Reference-Cited by-同舟云学术

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Adversarial Bandits Policy for Crawling Commercial Web Content

Published:2020-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of The Web Conference 2020
language:
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Author:

Han Shuguang¹,Bendersky Michael¹,Gajda Przemek²,Novikov Sergey²,Najork Marc¹,Brodowsky Bernhard²,Popescul Alexandrin³

Affiliation:

1. Google, USA

2. Google, Switzerland

3. Pinterest, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3366423.3380125

Reference48 articles.

1. The web changes everything

2. Learning a Hierarchical Embedding Model for Personalized Product Search

3. The Nonstochastic Multiarmed Bandit Problem

4. Tractable near-optimal policies for crawling

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LEARNING-based Focused WEB Crawler;IETE Journal of Research;2021-02-22

2. Smart algorithmic based web crawling and scraping with template autoupdate capabilities;Concurrency and Computation: Practice and Experience;2020-10-07

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