Cross-Domain User Personalization via Cluster Mapping
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Solution Overview
Problem
Search engines face challenges in providing personalized recommendations to users without a detailed history of online behavior and interests, especially when users are new to a specific domain, as existing systems struggle to accurately predict user preferences based on limited or absent data.
Innovation Solution
A system that maps users to attributes and products across different domains, creating predictive models to generate personalized predictions for users by identifying similar user types and utilizing proxy domains with similar properties when direct data is lacking, while ensuring privacy through encrypted data stores and offline model creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses existing user data from source domains to generate personalized predictions for target domains, then the personalization quality improves, but the system complexity increases due to cross-domain data mapping and model creation
Solution Approach 1:
The system segments users into discrete clusters based on shared attributes and behaviors, creating distinct user groups that can be independently analyzed and mapped across domains. This segmentation enables the complex cross-domain personalization task to be broken down into manageable cluster-based operations.
Solution Approach 2:
The patent introduces user clusters as an intermediary layer between source domain data and target domain predictions. These clusters act as mediators that bridge different domains, allowing the system to transfer behavioral patterns across domains without directly mapping raw data, thereby managing complexity while maintaining personalization accuracy.
2Speed
If the system creates predictive models offline using top scoring products, then the processing speed during user interaction improves, but the model creation time and computational resources increase
Solution Approach 1:
The system performs model creation and cluster analysis in advance during offline processing, preparing predictive models before users interact with the system. This preliminary action ensures that when users query the system, predictions can be generated rapidly using pre-computed models, sacrificing upfront time for subsequent speed gains.
3Adaptability or versatility
If the system stores detailed user behavior data across multiple domains, then the personalization capability improves, but the data privacy and security requirements increase
Solution Approach 1:
The system extracts only the essential behavioral patterns and attributes needed for personalization, separating these from detailed raw user data. By taking out only the necessary information for creating user clusters and predictive models, the system maintains personalization capability while reducing the storage and handling of sensitive personal information.
Solution Approach 2:
The patent creates abstract representations of user behavior through user clusters and predictive models, which are copies that capture essential patterns without containing actual personal data. These copied representations enable personalization while protecting user privacy by working with synthesized data rather than raw personal information.
Data Source
AI summary
A system stores a table mapping users to attributes, and stores a second table mapping the users to products associated with a source domain. The system determines a set of top scoring products for each of the attributes, and creates, using the top scoring products, a model that is predictive of an activity in a target domain, the target domain being separate from the source domain. The system detects a behavior from a particular user accessing the target domain, and generates a personalized prediction for the particular user based on the model, in response to the detecting the behavior.