Domain Persona Generation for Accurate Cross-Source Recommendations
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Solution Overview
Problem
Existing user profiling methods fail to provide a domain-specific representation of user behavior, leading to less accurate and reliable recommendations due to the inclusion of irrelevant data from different domains, which affects the efficacy of downstream machine learning applications.
Innovation Solution
A domain persona system that accesses data from various sources, including data portability APIs, message parsing systems, and user feedback to generate domain-specific personas, which are then used in downstream machine learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general user profiling methods are used to create user profiles, then comprehensive user data can be collected, but the accuracy and reliability of recommendations deteriorate due to inclusion of irrelevant data from different domains
Solution Approach 1:
The patent segments user data by creating domain-specific personas (e.g., travel persona, shopping persona, food persona) rather than using a single general profile. Each persona contains only data relevant to its specific domain, allowing comprehensive data collection across multiple domains while maintaining high accuracy within each domain through selective data inclusion.
2Reliability
If domain-specific personas are generated using multiple data sources, then recommendation accuracy improves, but system complexity increases due to integration of multiple data sources and processing steps
Solution Approach 1:
The patent creates a universal persona framework that can handle multiple domains (travel, shopping, food, etc.) using the same underlying structure and process. This multi-functional approach allows the system to generate domain-specific personas across different areas without creating separate complex systems for each domain, thus improving accuracy while controlling complexity through reuse of common infrastructure.
Solution Approach 2:
The patent introduces an intermediary persona layer between raw user data from multiple sources and the recommendation engine. This persona acts as a mediator that integrates and filters data from various sources (user interactions, third-party data, contextual information) into a unified domain-specific representation, simplifying the overall system architecture while maintaining high recommendation accuracy.
3Loss of information
If comprehensive user data is collected from multiple sources, then user behavior can be analyzed more thoroughly, but the time required for data processing and analysis increases
Solution Approach 1:
The patent extracts only the relevant data needed for each specific domain persona rather than processing all available user data. For example, when creating a travel persona, only travel-related data is extracted from the user's comprehensive data set, significantly reducing processing time while maintaining thorough analysis of travel behavior through focused data selection.
Data Source
AI summary
Systems and methods are provided for data intake of data from an ingestion service indicating a user's online or offline behavior with respect to a domain. A domain persona system may receive a request to access an ingestion service. The domain persona system may then access the ingestion service to obtain the data and identify a subset of the data relevant to the user's online or offline behavior with respect to the domain. The domain persona system may further generate insights with respect to the identified subset and output at least one of the identified subset and the generated insights to a system that updates the domain persona.