Popularity-Based Ranking System for Diverse Search Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search ranking systems are limited in their ability to provide accurate and relevant search results, as they primarily focus on text pages and link structures, failing to effectively account for user behavior and popularity metrics across various types of objects such as documents, videos, and images.
Innovation Solution
A system that tracks user interactions and access metrics to determine popularity-based rankings for objects, using machine learning to weight features like access frequency, user behavior, and other factors, and combines these rankings with existing algorithms to enhance search result accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional ranking systems use link structure and text page analysis, then web page ranking can be performed, but the system is limited to text pages and cannot effectively rank diverse object types such as videos, images, and documents
Solution Approach 1:
The patent applies universality by creating a ranking system that can handle multiple object types (web pages, videos, images, documents) through a unified approach. The system uses general popularity metrics (access count, access rate, user interactions) that apply across all object types, eliminating the need for separate ranking mechanisms for each object type while maintaining effective ranking capability.
Solution Approach 2:
The patent changes the ranking parameters from traditional link structure and text analysis to popularity-based metrics including access count, access rate, and user interaction data. This parameter transformation enables the system to rank diverse object types effectively by focusing on actual user engagement patterns rather than structural relationships.
2Measurement precision
If the system tracks detailed user interaction data and access metrics, then popularity-based ranking accuracy improves, but data collection and processing complexity increases
Solution Approach 1:
The patent extracts and processes only the essential popularity metrics from vast amounts of user interaction data. Instead of analyzing every detail of user behavior, the system extracts key aggregated metrics such as access count, access rate, and interaction patterns, which provide sufficient precision for accurate popularity-based ranking while significantly reducing data processing complexity.
Solution Approach 2:
The system uses automatically collected access logs and interaction data that are generated as part of normal system operation. The popularity metrics are derived from data that the system already collects for other purposes, eliminating the need for separate complex data collection mechanisms while maintaining measurement precision.
3Reliability
If the system uses access count and access rate metrics, then ranking reflects actual user popularity, but fraudulent or spammy content can inflate rankings
Solution Approach 1:
The patent implements feedback mechanisms that monitor user interaction patterns and adjust rankings accordingly. The system analyzes quality indicators such as time spent on objects, interaction types, and access patterns to distinguish between legitimate popular content and fraudulent content. This feedback loop allows the system to refine rankings and mitigate the impact of spammy or fraudulent objects.
Solution Approach 2:
The system changes from using simple access count to a composite metric that includes access rate, interaction quality, and temporal patterns. By analyzing the rate of access and the nature of user interactions rather than just total count, the system can identify and downweight fraudulent content while maintaining accurate rankings for legitimate popular objects.
Data Source
AI summary
A unique ranking system and method that facilitates improving the ranking and ordering of objects to further enhance the quality, accuracy, and delivery of search results in response to a search query. The system and method involve monitoring and tracking an object in terms of the number of times it's been accessed and optionally by whom, when, for how long, and an access rate. The user's interaction with the object can be tracked as well. By tracking the objects, a popularity measure can be determined. Popularity based rankings can be computed based on the popularity measure or some function thereof. The popularity measure can be affected by the access time, who accessed it, access duration or the user's interaction with the object upon access. The popularity based rankings can be utilized by a search component to improve the quality and retrieval of search results.