Trending Phrase Relevance Scoring via Shortened URL Tracking
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Solution Overview
Problem
It is challenging to identify, track, and analyze the sharing and forwarding of content across multiple users, devices, and sources due to the proliferation of mobile devices and social networking, making it difficult to determine trending and relevant content.
Innovation Solution
The system tracks user interactions through methods such as clicking on shortened URLs, shares relevance scores for digital resources, identifies trending phrases, and measures user influence by analyzing clicks and sharing actions across an aggregate of users, providing search results and recommended URLs based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system aggregates user interaction data across multiple users, devices, and sources to identify trending content, then the accuracy of identifying relevant and trending content is improved, but the complexity of tracking and analyzing the data increases
Solution Approach 1:
The patent introduces shortened URLs as intermediary elements that mediate between the original content and user interactions. These shortened URLs serve as tracking tokens that simplify the complex task of monitoring content sharing across multiple users and devices. Instead of directly tracking numerous original URLs and their interactions, the system uses these condensed intermediaries to aggregate and analyze sharing patterns, thereby reducing tracking complexity while maintaining measurement precision.
Solution Approach 2:
The system merges multiple user interaction data streams into a unified analysis framework. By combining click data, sharing data, and engagement metrics from numerous users and devices into a single aggregated dataset, the system identifies trending content patterns that would be difficult to detect individually. This merging approach improves measurement precision by leveraging collective behavior data while managing complexity through centralized processing.
2Measurement precision
If the system tracks detailed user interactions including clicks and shares across multiple digital resources, then the ability to generate accurate relevance scores is improved, but the amount of data to be processed increases
Solution Approach 1:
The system extracts only the essential interaction data needed for relevance scoring from the vast amount of raw user interaction data. By selectively extracting meaningful signals such as click patterns, sharing frequency, and engagement duration while filtering out redundant information, the system maintains high scoring accuracy without being overwhelmed by the complete volume of interaction data.
Solution Approach 2:
The system performs preliminary aggregation and classification of user interaction data before detailed analysis. By pre-processing interaction records to group them by content, user, and interaction type, the system prepares the data in an optimized format that reduces the computational burden during relevance score generation, thereby managing data volume while preserving measurement precision.
3Adaptability or versatility
If the system analyzes user behavior patterns across diverse content types and sources, then the versatility of content recommendation is improved, but the difficulty of detecting and measuring user preferences increases
Solution Approach 1:
The system implements a universal tracking framework that handles multiple content types (articles, videos, images) and sources (social media, websites, apps) through a unified data collection mechanism. This multi-functional approach allows the system to detect user preferences across diverse content while maintaining consistent measurement standards, thereby improving recommendation versatility without proportionally increasing detection difficulty.
Solution Approach 2:
The system transforms diverse user interaction behaviors into standardized preference parameters that can be universally measured and compared. By converting various interaction types (clicks, shares, time spent) into normalized preference scores, the system simplifies the detection and measurement of user preferences across different content types and sources, making the analysis more manageable while preserving recommendation versatility.
Data Source
AI summary
The present disclosure is directed to a method for identifying which phrases are trending across an aggregate of users that are relevant to a specific user. The method may include receiving, by a server, identification of a user. The server may identify a plurality of phrases that are trending upwards based on velocity of clicks to content containing, related to or associated with the plurality of phrases. The server may identify trending or temporally popular phrases based on aggregating multiple users' interactions with an aggregate of content. The server may determine a relevance score for each phrase of the plurality of phrases that are trending upwards based on identification of the user and actions of the user on content associated with each phrase, such as user clicking on content identifying or related to each phrase. The server may identify one or more phrases of the plurality of phrases based on relevance score.