Web Crawler Scheduling via Popularity Decay Prediction

The system performs preliminary prediction of popularity and decay rate for newly created web resources before they are added to the crawling queue. By estimating these parameters in advance using machine learning models, the crawler can prioritize resources that are likely to be most important, rather than waiting for actual popularity data to accumulate over time.

The system uses feedback from actual web resource usage patterns to refine the machine learning models that predict popularity and decay rate. By continuously learning from real-world data about which pages are visited most frequently and how their popularity changes over time, the prediction accuracy improves, enabling better prioritization decisions.

The system applies different crawling strategies to different web resources based on their predicted importance. Rather than treating all pages equally, the crawler adjusts its behavior locally for each resource, prioritizing pages with high predicted popularity and low decay rates while reducing attention to less important pages. This localized differentiation improves both search quality and resource efficiency.

The system changes the crawling parameters (such as visit frequency and priority) based on predicted popularity and decay rate parameters. By dynamically adjusting these parameters according to the predicted characteristics of each web resource, the system optimizes the balance between comprehensive coverage and efficient resource utilization.