Base User Profile for Cross-Platform Content Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional content recommendation systems struggle to provide users with relevant content, especially when their interests have not been identified, as they rely on user profiles that are fragmented, reactive, and application-centric, failing to capture long-term interests and integrating interests across different platforms effectively.
Innovation Solution
A method for recommending content using a base user profile determined from the activities of representative users, which includes selecting representative users based on selection criteria, analyzing their activity information to rank interests, and recommending content based on these ranked interests, even when the user's own interests are not identified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user profiles are constructed based on declared interests and demographics, then user profiling can be established, but the profiles become fragmented and lack coherent understanding of user preferences across different applications
Solution Approach 1:
The patent introduces a universal base profile that serves as a common foundation across multiple applications. This base profile captures user interests in a standardized manner that can be consistently applied across different application contexts, enabling coherent understanding of user preferences without requiring application-specific profiles. The base profile acts as a multi-functional foundation that supports both immediate application needs and long-term user understanding.
Solution Approach 2:
The system performs preliminary actions by proactively identifying and categorizing user interests before they manifest in specific application interactions. By analyzing user behavior patterns and interests in advance across multiple applications, the system builds a comprehensive base profile that predicts and anticipates user needs, rather than reacting to interests after they are expressed in isolated application contexts.
2Productivity
If content recommendation relies on traditional CTR metrics and short-term user interactions, then immediate user interests can be captured, but long-term interests and user retention are compromised
Solution Approach 1:
The patent implements continuous monitoring and analysis of user interactions across multiple applications over extended periods. Rather than relying on isolated CTR metrics, the system continuously updates the base profile by analyzing diverse user behaviors including time spent on content, navigation patterns, and interaction sequences. This continuous action enables the system to distinguish between transient interests and sustained preferences, maintaining both rapid recommendation capability and long-term user understanding.
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns to identify emerging interests before they fully manifest as sustained preferences. By analyzing early-stage interactions and behavioral indicators across multiple applications, the system proactively builds a comprehensive view of user interests that predicts long-term preferences, rather than waiting for explicit long-term interest declarations.
3Adaptability or versatility
If user interests are detected in isolated application settings, then application-specific interests can be captured, but the overall user interest representation becomes fragmented
Solution Approach 1:
The patent merges interest data from multiple isolated application settings into a unified base profile. By combining and contextualizing interest information across different applications, the system creates a coherent representation of overall user preferences. The base profile integrates patterns from diverse application contexts, eliminating fragmentation while preserving application-specific nuances through hierarchical organization of interest data.
Solution Approach 2:
The base profile serves as an intermediary layer between isolated application-specific interests and the overall user interest representation. It mediates by aggregating and contextualizing data from multiple applications, translating fragmented application-specific signals into a coherent unified profile. This intermediary structure enables the system to maintain application-specific detail while building comprehensive cross-application understanding.
4Ease of operation
If content recommendation uses traditional application-centric approaches, then immediate user needs can be met, but integration of interests across different platforms becomes difficult
Solution Approach 1:
The base profile serves as a universal data structure that can be consistently applied across multiple applications and platforms. It provides a standardized representation of user interests that works uniformly regardless of the specific application context, enabling seamless integration of cross-platform interest data while maintaining immediate responsiveness to user needs in each individual application.
Solution Approach 2:
The system performs preliminary consolidation of user interest data across platforms before it is needed for specific content recommendations. By pre-aggregating and contextualizing interest information from multiple sources, the system ensures that cross-platform integration is already complete and ready for immediate use, eliminating delays while maintaining comprehensive cross-platform understanding.
Data Source
AI summary
A method and system for recommending content to a user whose interest(s) has not been identified is disclosed. A base user profile may be created for association with the user. The base user profile may be created by generating a list of ranked interests of a set of representative users. The list of ranked interests may be generated based on activity information obtained for the set of representative users. Content may be recommended to the user based on the base user profile.