Similarity Profile Generation for User Profile Modification
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Solution Overview
Problem
Current systems lack an effective method to accurately select content for presentation on client devices by supplementing user information with data from similar user profiles, leading to suboptimal content recommendation and user engagement.
Innovation Solution
The system analyzes activity data from multiple client devices to generate network profiles, similarity profiles, and modify user profiles based on these profiles, enabling more accurate content selection by incorporating data from similar user profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content selection is based solely on individual user information, then the system is simple to operate, but content recommendation accuracy is suboptimal
Solution Approach 1:
The patent combines individual user profiles with network profiles that aggregate data from multiple users. The system merges these different data sources to create enhanced user profiles that leverage both personal preferences and collective behavior patterns, thereby improving content selection accuracy without requiring complete reconstruction of the system architecture
Solution Approach 2:
The system performs preliminary analysis of user activity data to generate network profiles before content selection occurs. By pre-processing and aggregating user behavior data into structured network profiles, the system prepares recommendation data in advance, reducing computational complexity during actual content selection while maintaining high accuracy
2Measurement precision
If the system incorporates data from multiple user profiles, then content recommendation accuracy improves, but data processing time increases
Solution Approach 1:
The system performs preliminary aggregation of user activity data to create network profiles in advance, before content selection is needed. This pre-processing step consolidates data from multiple users into structured formats, reducing the computational burden during actual recommendation generation and minimizing processing time delays
Solution Approach 2:
The patent segments the data processing into distinct components: individual user profile analysis, network profile aggregation, and final content selection. By dividing the complex processing task into manageable segments that can be executed independently and cached, the system reduces overall processing time while maintaining comprehensive data analysis
3Productivity
If the system analyzes activity data from multiple client devices, then user engagement improves, but computational resources increase
Solution Approach 1:
The system merges activity data from multiple client devices into aggregated network profiles that capture collective user behavior patterns. By combining data at the network level rather than processing each user separately, the system achieves better user engagement through more accurate recommendations while reducing redundant computational operations across multiple devices
Solution Approach 2:
The patent creates simplified representations (copies) of user behavior patterns through network profiles that capture essential characteristics without storing complete raw data from all users. These profile copies enable efficient analysis and comparison while consuming fewer computational resources than processing full activity datasets
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with client devices and entities. A network profile associated with the client devices and the entities may be generated based upon the event information. A similarity profile associated with the client devices may be generated based upon the network profile. The similarity profile may be indicative of one or more similarity scores associated with a first client device and one or more client devices. A user profile associated with the first client device may be modified, based upon the similarity profile and/or one or more user profiles associated with the one or more client devices, to generate a modified user profile. Content may be selected for presentation via the first client device based upon the modified user profile.