Client-Side Search Ranking Using Behavior Data
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Solution Overview
Problem
Conventional client-side search applications rely on limited article attributes and history data, which can degrade user experience by not accurately reflecting user interest in search results.
Innovation Solution
The system determines a ranking score for articles based on duration data, access data, URL data, and trajectory data associated with the article, using a monitoring engine to collect client-side behavior data and a query processor to generate implicit queries for improved search ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional client-side search applications use limited article attributes and history data for ranking, then the search system remains simple and fast, but the accuracy of reflecting user interest deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and storing client-side behavior data (duration data, access data, URL data, trajectory data) in advance before the search query is executed. This pre-collected data is then used to improve ranking accuracy without adding complexity to the actual search execution process.
Solution Approach 2:
The patent introduces client-side behavior data as an intermediary element that mediates between the user's actual interest and the search ranking system. This intermediary data layer allows the system to reflect user interest more accurately without requiring direct complex analysis of user intent during the search process.
2Measurement precision
If the system collects and processes multiple types of client-side behavior data (duration, access, URL, trajectory), then the search ranking accuracy improves, but the data processing complexity and time increase
Solution Approach 1:
The system collects and stores multiple types of client-side behavior data in advance before the search is executed. By performing this data collection preliminarily, the system avoids the time cost of gathering data during the actual search process, thus improving ranking accuracy without proportionally increasing processing time.
Solution Approach 2:
The client-side application automatically collects and stores its own behavior data (duration, access patterns, URL information, trajectory) without requiring external intervention during the search process. This self-service approach to data collection reduces the processing burden on the search system itself.
Data Source
AI summary
Systems and methods that improve client-side searching are described. In one aspect, a system and method for identifying an article, and determining a ranking score for the article based at least in part on duration data, access data, URL data, or trajectory data associated with the article is described.