Adversarial Bandit Recrawling for Dynamic Offer Page Freshness
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Solution Overview
Problem
Conventional web crawler recrawl strategies for highly dynamic content are inefficient, leading to suboptimal use of network resources and staleness of content due to assuming static parameter values, failing to adapt to the dynamic nature of webpage changes.
Innovation Solution
Implementing a K-armed adversarial bandit algorithm with reinforcement learning to dynamically select recrawl strategies based on estimated parameters like click rate, impression rate, and change rate, using both history data and metadata for improved freshness under resource constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional recrawl strategies assume static parameter values, then the recrawl process is simple to implement, but the content freshness deteriorates due to inability to adapt to dynamic webpage changes
Solution Approach 1:
The patent transforms static recrawl strategies into dynamic ones by continuously estimating parameters (click rate, impression rate, change rate) and adjusting recrawl rates accordingly. The system adapts to changing webpage characteristics by updating parameter estimates over time, ensuring content freshness while responding to dynamic web environment changes.
Solution Approach 2:
The patent implements feedback mechanisms where recrawl outcomes and observed webpage changes are used to update parameter estimates. The system learns from past recrawl performance and uses this feedback to improve future recrawl decisions, creating a closed-loop control system that optimizes content freshness.
2Reliability
If recrawl operations are performed frequently to ensure content freshness, then the accuracy of webpage information is improved, but the consumption of network resources increases
Solution Approach 1:
The patent changes the recrawl rate parameter dynamically based on estimated parameters (click rate, impression rate, change rate) for each webpage. High-priority pages with frequent changes receive higher recrawl rates, while low-priority pages receive lower rates, optimizing the balance between content accuracy and network resource consumption.
Solution Approach 2:
The patent applies different recrawl strategies and rates to different webpages based on their individual characteristics. Each webpage receives a customized recrawl rate determined by its specific parameter estimates, rather than applying a uniform recrawl rate to all pages, thus optimizing resource allocation locally for each page.
3Reliability
If multiple recrawl strategies are combined to optimize content freshness, then the robustness under resource constraints is improved, but the complexity of strategy selection increases
Solution Approach 1:
The patent implements self-service through automated parameter estimation and strategy selection. The system automatically estimates parameters from historical data and metadata, selects appropriate recrawl strategies based on these estimates, and adjusts recrawl rates without manual intervention, reducing the perceived complexity for users.
Solution Approach 2:
The patent creates a universal recrawl framework that can handle multiple webpage types and scenarios through a single system. The framework estimates parameters and selects strategies that work across different webpage characteristics, making the system multi-functional and adaptable to various content types without requiring separate specialized strategies.
Data Source
AI summary
Techniques of generating recrawl policies for commercial offer pages include generating a multiple strategy approach using a number of different strategies. In some implementations, each strategy is an arm of a K-armed adversarial bandits algorithm with reinforcement learning. Moreover, in some implementations, the multiple strategy approach also uses a machine learning algorithm to estimate parameters such as a click rate, impression rate, and likelihood of price change, i.e., change rate, which was assumed known in the conventional approaches.