Relevance Scoring via User Interaction Pattern Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
It is challenging to effectively track and analyze the sharing and forwarding of content across multiple users, devices, and sources due to the complexity of user interactions with digital resources on the internet.
Innovation Solution
The system and method for tracking user interactions involve receiving user actions such as clicks and shares on digital resources, extracting keywords, and classifying patterns to generate a relevance score for digital resources, providing search results based on user interaction data, identifying trending phrases, and tracking user influence by analyzing encoded URL clicks and shares.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system tracks and analyzes user interactions across multiple users, devices, and sources, then the accuracy of relevance scoring improves, but the system complexity increases
Solution Approach 1:
The system segments user interactions into distinct action types (clicks, shares, forwards) and organizes them by user, device, and source. This segmentation allows the system to process complex interaction data in manageable units, tracking each action type separately while maintaining overall accuracy in relevance scoring without overwhelming system complexity.
2Measurement precision
If the system processes and classifies patterns from multiple user actions and keywords, then the quality of search results improves, but the processing time increases
Solution Approach 1:
The system performs preliminary classification of user actions into standardized patterns and pre-processes keywords before generating search results. By classifying action patterns and organizing keywords in advance, the system reduces the computational burden during actual search operations, maintaining high search result quality while minimizing processing time delays.
3Adaptability or versatility
If the system generates relevance scores based on classified patterns, then the personalization of content delivery improves, but the computational resources required increase
Solution Approach 1:
The system generates relevance scores by transforming classified action patterns and keyword data into standardized relevance parameters. This parameter transformation approach enables personalized content delivery by converting complex interaction data into manageable relevance scores that can be efficiently used for content ranking and recommendation, reducing the computational burden while maintaining personalization quality.
Data Source
AI summary
The present disclosure is directed to systems and methods that score content, URLs, domains, phrases or any entity based on an expected relevance to an individual user which may be based on that user's previous engagement with digital resources. A server receives identification of a plurality of actions of a user, which may include a click by the user on a link associated with a digital resource of a plurality of digital resources. The server may receive identification of actions of the user to share one or more digital resources of the plurality of digital resources. The server may identify a plurality of keywords from content of the plurality of digital resources, classify patterns from the actions of the users and the keywords and generate, based on the pattern classification, a relevance score responsive to receiving a user identifier and a digital resource keyword.