Collective Profile Refinement for Multi-Account Recommendation Consistency
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Solution Overview
Problem
Existing recommendation systems face challenges in providing personalized recommendations for users with multiple accounts or collective profiles, as attributes and goals may conflict, and maintaining privacy while resolving inconsistencies in collective profile data.
Innovation Solution
A server computer system that manages individual and collective profiles by authenticating login requests, refining attributes and goals based on user responses, and generating recommendations while ensuring consistency across linked profiles and maintaining privacy settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system integrates data from multiple individual accounts into a collective profile, then the personalization of recommendations is improved, but inconsistencies and conflicts between account attributes may arise
Solution Approach 1:
The system segments the collective profile into multiple individual account profiles, each with its own attributes and goals. This allows the system to maintain the benefits of personalized recommendations while managing inconsistencies at the individual profile level rather than the collective level.
Solution Approach 2:
The system introduces an intermediary layer that manages the relationship between individual profiles and the collective profile. This intermediary handles attribute conflicts and goal inconsistencies by coordinating between individual account data and collective recommendations.
2Measurement precision
If the system accesses and processes data from multiple linked profiles, then the accuracy of recommendations is improved, but privacy protection becomes more complex
Solution Approach 1:
The system applies different privacy rules to different parts of the profile data. Sensitive attributes are protected with stricter access controls while less sensitive data can be shared across profiles for improved recommendation accuracy.
Solution Approach 2:
The system establishes privacy preferences and access rules in advance for each profile before data processing occurs. This preliminary configuration simplifies subsequent data access and processing while maintaining privacy protection.
3Manufacturing precision
If the system resolves conflicts between account attributes in collective profiles, then the quality of recommendations is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of profile data during off-peak times to pre-resolve common conflicts and prepare recommendation templates. This reduces the computational burden and processing time when generating real-time recommendations.
Solution Approach 2:
The system resolves only the most critical conflicts between account attributes rather than attempting to resolve all possible inconsistencies. This partial resolution approach maintains recommendation quality while reducing processing time.
Data Source
AI summary
A method of generating recommendations for a collective profile, the collective profile being linked to a first profile and a second profile. The method may include authenticating a login request from an end user device in association with the collective profile based on credentials associated with the first profile, identifying a query from among a plurality of queries and transmit the query to the end user device, receiving a response to the query, determining that the response is not inconsistent with one or more earlier responses received in association with the second profile and, in response, refining at least one attribute or goal in the collective profile based on the response to generate and store a refined collective profile, and determining whether the refined collective profile results in a new recommendation and, if so, outputting the new recommendation.