Personalized Content Recommendation Using Semantic Tag Matching
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Solution Overview
Problem
Employers struggle to effectively engage employees in health and wellness programs, leading to underutilization of available benefits, as they lack the ability to understand which programs are most impactful and fail to grasp the full set of offerings available to their employees.
Innovation Solution
A system and method for generating personalized health and wellness content using a user profile, which includes a recommendation engine that selects content items based on user characteristics and history, applying natural language processing to suggest health programs and marketplace items that align with the user's interests and needs, utilizing a common taxonomy for tagging and a vectorized item corpus for similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If employers provide comprehensive health and wellness programs to employees, then the availability of health benefits is improved, but employee engagement and utilization remain low
Solution Approach 1:
The system segments the comprehensive health benefits portfolio into personalized subsets for each employee based on their profile data, preferences, and needs. Instead of presenting all available programs at once, the recommendation engine divides and delivers tailored content, making the overwhelming array of benefits manageable and relevant to individual employees.
Solution Approach 2:
The system applies local quality by customizing the presentation of health benefits to match each employee's specific characteristics, preferences, and health goals. The recommendation engine analyzes individual user profiles and delivers personalized recommendations, ensuring that each employee receives content with quality and relevance specific to their local context rather than generic information.
2Adaptability or versatility
If employers provide extensive health program offerings, then the variety of benefits is improved, but the ability to understand which programs are most impactful deteriorates
Solution Approach 1:
The system implements feedback mechanisms by tracking employee interactions with recommended content, monitoring engagement metrics, and using this data to refine future recommendations. The recommendation engine continuously learns from employee responses and behavior patterns, providing feedback loops that improve the system's ability to identify which programs are most impactful for different employee segments.
Solution Approach 2:
The system enables self-service by automatically analyzing employee profiles, preferences, and engagement data to generate personalized recommendations without requiring employer intervention. The recommendation engine autonomously processes vast amounts of data to identify impactful programs, freeing employers from manually assessing which benefits resonate with employees.
3Productivity
If a personalized recommendation system is implemented, then employee engagement is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary recommendation engine that acts as a mediator between the complex backend data infrastructure and the simple user-facing interface. This intermediary layer processes complex data analysis, profile matching, and recommendation generation internally while presenting simple, easy-to-navigate personalized content to employees, thereby hiding system complexity from users.
Solution Approach 2:
The system performs preliminary actions by pre-processing and structuring employee data, building user profiles, and preparing recommendation algorithms in advance. The recommendation engine pre-analyzes employee characteristics and organizes health benefit information beforehand, so that when employees access the system, personalized recommendations are readily available without requiring complex real-time processing during user interactions.
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.