Hybrid Two-Phase Boosting for Search Ranking
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Solution Overview
Problem
Conventional search engine ranking systems fail to seamlessly integrate both online and offline features, leading to suboptimal ranking accuracy and responsiveness, as online features are limited by real-time data availability and offline features are impractical for real-time computation.
Innovation Solution
A hybrid two-phase boosting method is employed, where a decision tree boosting algorithm trains both online and offline ranking functions, allowing for the combination of online and offline features to produce a unified ranking score, with offline scores computed periodically and stored for query-time integration with real-time online scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If only online features are used for ranking, then real-time responsiveness is improved, but ranking accuracy deteriorates due to limited data availability
Solution Approach 1:
The patent combines online features (real-time data) and offline features (pre-computed data) into a unified ranking system. The hybrid two-phase boosting method integrates both feature types to produce a final ranking score, allowing the system to leverage the speed of online features while incorporating the accuracy benefits of offline features for improved ranking precision.
2Measurement precision
If offline features are used for ranking, then ranking accuracy is improved, but computation time increases making real-time processing impractical
Solution Approach 1:
The patent segments the ranking computation into two distinct phases: an offline phase where complex features are pre-computed and stored, and an online phase where only lightweight scoring and combination operations are performed. This segmentation allows accurate offline feature computation to be separated from real-time ranking operations, reducing online computation time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary computation of offline features in advance, storing them for later use during online ranking operations. By pre-computing these features offline and caching the results, the system eliminates the need for time-consuming real-time computation of complex features, enabling fast online ranking while maintaining high accuracy.
3Device complexity
If conventional ranking systems use only one type of features, then system complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent creates a universal ranking framework that can handle multiple feature types (online and offline) through a unified two-phase boosting approach. The system is designed to be multi-functional, accommodating diverse feature sources and computation methods while maintaining a consistent ranking interface, thereby improving adaptability without proportionally increasing complexity.
Data Source
AI summary
Approaches are described for ranking multiple products or other items, such as products obtained in response to a search request submitted to a server. The ranking system determines a ranking score for the products based on both data available online and item data that must be computed offline due to longer computation time or unavailability of data. The ranking score can be used to rank the products and determine which products are the most relevant to the user. A hybrid boosting method is used to first train an online ranking function to produce an online ranking score for the item. In the second phase, an offline ranking function is trained to produce a second ranking score for the item. The online rank score is combined with the offline rank score at query time to produce a combined rank for the items in the search results.