Trending Content Identification System Using Multi-Source Data Aggregation
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Solution Overview
Problem
The abundance of online content across various domains makes it difficult and time-consuming for users to keep up with the latest trending content, as existing methods lack an efficient way to identify and aggregate popular content from diverse sources such as search engines, social networks, and browsing data.
Innovation Solution
A computer-implemented method and system that processes network activity data from search engines, social networks, and browsing history to identify trending content candidates across domains, aggregating and filtering them to provide a subset of trending content in response to user requests, utilizing components like query log processing, social data processing, and browsing history processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually visit numerous websites to keep up with latest content, then they can access comprehensive information, but it requires significant time and effort
Solution Approach 1:
The patent combines multiple data sources (search engine query logs, social network data, browsing history) into a single trending content identification system. This merging of diverse information sources allows users to access comprehensive trending content across multiple domains through one interface, eliminating the need to manually visit numerous websites while maintaining complete information coverage
Solution Approach 2:
The system acts as an intermediary between users and the vast online content ecosystem. By introducing this intermediate layer that automatically aggregates and analyzes data from multiple sources, the system retrieves trending content without requiring users to directly interact with numerous individual websites, thus preserving information completeness while dramatically reducing time investment
2Measurement precision
If the system analyzes data from multiple sources to identify trending content, then the accuracy of trending content identification improves, but the system complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: query log processing, social network data processing, and browsing history processing. Each segment handles a specific data source independently, then results are aggregated. This segmentation maintains high identification accuracy by thoroughly analyzing each source while reducing overall system complexity through modular, manageable components
Solution Approach 2:
The aggregation component serves as an intermediary that receives processed results from multiple independent processing modules and combines them into final trending content identification. This intermediary layer coordinates the complex multi-source analysis while presenting a simplified unified output, thereby maintaining high precision through comprehensive analysis while managing system complexity through structured intermediate processing
3Loss of information
If the system aggregates trending content from multiple domains, then the comprehensiveness of content coverage improves, but the difficulty of processing and organizing data increases
Solution Approach 1:
The system segments content aggregation by domain, creating separate processing streams for different domains. This segmentation allows comprehensive coverage of multiple domains while organizing data in a structured, manageable way that reduces processing difficulty compared to handling all domains as a single undifferentiated mass
Solution Approach 2:
The patent applies domain-specific processing characteristics to different domains, tailoring the aggregation approach to each domain's unique properties. This local quality approach ensures comprehensive coverage of each domain while simplifying processing by applying appropriate domain-specific methods rather than a one-size-fits-all approach, thereby reducing overall organizational difficulty
Data Source
AI summary
Systems and methods for identifying trending content on one or more domains is presented. In response to receiving a request for trending content on each domain of a set of domain, network activity data corresponding to network activity of a recent period of time is obtained. According to various embodiments, the network activity data corresponds to activity in the immediately previous time period and includes any of query logs from one or more search engines, social data from one or more social network sites, and browsing data corresponding to the browsing history of a plurality of computer users. Trending content from the network activity data for each domain of the set of domains is identified and the identified content is returned in response to the received request.