Semantic Content Personalization for Real-Time User Engagement
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Solution Overview
Problem
Employers struggle to effectively engage employees in health and wellness programs, leading to underutilization of available benefits, and there is a need for personalized content delivery to improve health outcomes and optimize healthcare spending.
Innovation Solution
A system and method for generating personalized health and wellness content based on user profiles, using a recommendation engine to suggest content items semantically similar to the user's interests and history, incorporating a content index and natural language processing to provide real-time recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personalized content delivery systems are implemented, then user engagement with health benefits is improved, but device complexity and system infrastructure requirements increase
Solution Approach 1:
The patent introduces a recommendation engine as an intermediary component that sits between the content library and the user interface. This engine processes user profiles, analyzes preferences, and generates personalized content recommendations automatically, thereby improving user engagement without requiring complex manual configuration or intervention from users or system administrators.
Solution Approach 2:
The system implements self-service mechanisms where the recommendation engine automatically updates user profiles based on interaction history, dynamically generates personalized content lists, and adapts recommendations without human intervention. This automation reduces the operational burden on users and simplifies the overall system operation despite the underlying complexity.
2Measurement precision
If comprehensive user profiling and content analysis are performed, then content personalization accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and tagging content items with metadata and keywords during content ingestion. User profiles are also pre-analyzed to establish baseline preferences. This preparation work is done in advance so that when personalization queries are executed, the system can quickly match pre-tagged content with pre-analyzed user profiles, significantly reducing real-time processing time while maintaining high personalization accuracy.
Solution Approach 2:
The content library is segmented into categories and tagged with multiple metadata labels. The recommendation engine processes different segments independently and combines results, allowing parallel processing that reduces overall computation time. This segmentation enables the system to handle large volumes of content without requiring exhaustive analysis of every item for each user query.
3Ease of operation
If real-time content recommendations are provided, then user experience and engagement are improved, but system response time requirements and computational load increase
Solution Approach 1:
The patent applies local quality by providing different levels of personalization for different content types and user contexts. Not all content requires full real-time personalization processing. The system identifies high-priority content areas that benefit most from real-time recommendations and allocates computational resources accordingly, while using pre-computed recommendations for less time-sensitive content, thereby maintaining good user experience without overwhelming system resources.
Data Source
AI summary
A computer-implemented method of selecting content items from a collection of content items in a content system. The method includes determining at least one characteristic of a user from a corresponding user profile and determining at least one previously selected content item selected from the collection of content items by the user from a user history. A first set of tags is generated comprising at least one tag associated with the at least one characteristic and at least one previously selected content item. The first set of tags is used as input to query an index of the collection of content items with which are associated a second set of tags that are semantically similar to the first set of tags. The result of the query is a list of content items for the user associated with the corresponding user profile.