Predictive Crawling for Commercial Web Content
Abstract
Web crawlers spend significant resources to maintain freshness of their crawled data. This paper describes the optimization of resources to ensure that product prices shown in ads in a context of a shopping sponsored search service are synchronized with current merchant prices. We are able to use the predictability of price changes to build a machine learned system leading to considerable resource savings for both the merchants and the crawler. We describe our solution to technical challenges due to partial observability of price history, feedback loops arising from applying machine learned models, and offers in cold start state. Empirical evaluation over large-scale product crawl data demonstrates the effectiveness of our model and confirms its robustness towards unseen data. We argue that our approach can be applicable in more general data pull settings.
Research Areas
