Peer-Based Entity Selection Through Exchange Profile Intersection
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Solution Overview
Problem
Existing recommendation engines expend significant computing resources processing large volumes of user data to provide recommendations, often failing to accurately identify user preferences and requiring extensive user filtering, thus inefficiently consuming resources.
Innovation Solution
A peer-based selection system using machine learning generates exchange profiles for users based on historical data from a small number of acquaintances, determining relevant entity selections through an intersection of these profiles, thereby reducing resource consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a recommendation engine processes large volumes of user data to provide recommendations, then the coverage of recommendations is improved, but the computing resources and time consumed increase significantly
Solution Approach 1:
The patent segments the recommendation problem into two parts: (1) pre-computing peer exchange profiles using historical data, and (2) performing quick intersection operations between the user profile and peer profiles. This segmentation allows heavy computation to be done once in advance, while the actual recommendation generation requires minimal processing time.
Solution Approach 2:
The system performs preliminary actions by pre-computing exchange profiles for multiple peers using their historical exchange data before the actual recommendation is needed. These pre-computed profiles are stored and can be quickly intersected with the user's exchange profile to generate recommendations, avoiding the need to process all raw data at recommendation time.
2Adaptability or versatility
If a recommendation engine processes large volumes of user data to provide recommendations, then the coverage of recommendations is improved, but the computing resources consumed increase significantly
Solution Approach 1:
The patent extracts only the essential characteristics from large volumes of user exchange data to create compact exchange profiles. Instead of processing and storing all raw exchange data, the system extracts key features such as exchange categories, frequencies, and patterns, creating condensed representations that require minimal storage and processing resources.
Solution Approach 2:
The system performs preliminary actions by pre-computing exchange profiles for multiple peers using their historical exchange data. These pre-computed profiles are stored and can be quickly intersected with the user's exchange profile to generate recommendations, avoiding the need to re-process large volumes of raw data at recommendation time.
3Ease of operation
If a recommendation engine provides tourist-friendly recommendations for a particular city or area, then the general appeal is improved, but the accuracy of local resident preferences decreases
Solution Approach 1:
The patent introduces peer exchange profiles as intermediaries between the recommendation engine and the user. Instead of directly analyzing tourist attractions or making assumptions about user preferences, the system uses the exchange profiles of local residents (peers) as mediators to infer relevant recommendations. This intermediary approach captures authentic local preferences without requiring the system to understand nuanced local knowledge.
4Measurement precision
If a recommendation engine requires extensive user filtering to identify relevant recommendations, then the precision of recommendations is improved, but the time and effort required from users increases
Solution Approach 1:
The patent implements self-service by automatically performing the filtering function that would otherwise require user effort. The system computes the intersection between the user's exchange profile and peer exchange profiles, automatically identifying recommendations that match the user's preferences based on peer behavior patterns. This eliminates the need for users to manually filter through extensive lists of recommendations.
Data Source
AI summary
In some implementations, a system may generate a first exchange profile for a first user and a second exchange profile for a second user. The first exchange profile may identify one or more characteristics of an exchange behavior of the first user based on exchange data of the first user, and the second exchange profile may identify one or more characteristics of an exchange behavior of the second user based on exchange data of the second user. The system may receive, from a first device of the first user, a request for a selection for the first user. The system may determine, based on the first exchange profile and the second exchange profile, an entity that is relevant to the first user. The system may transmit, to the first device of the first user, information indicating the selection for the first user, where the selection identifies the entity.