Building a Personalized Recommendation Engine for an On-Demand Fitness Platform
35% Increase in user engagement
driven by personalized workout recommendations
25% Improvement in user retention
through AI-based content personalization and discovery
About the Client
The client is a global leader in on-demand fitness content, delivering personalized digital workout programs accessible anytime and anywhere. The platform supports both structured training and flexible wellness experiences through a seamless, high-quality digital experience.
Industry
Wellness and Fitness
Years in business
14 years
Employee count
50+
Presence
United States
“Forgeahead's recommendation engine transformed the user experience. We've seen better engagement, longer sessions, and more consistent content discovery.”
Michael Turner
Chief Product Officer
the need
A leading on-demand fitness platform offering diverse workout content including yoga, HIIT, and meditation sought to improve user engagement and retention through personalized recommendations.
Despite a large content library, the absence of effective personalization led to declining retention rates and limited content discovery. Users were not consistently exposed to relevant workouts based on their preferences and behavior.
The platform required a recommendation system capable of improving content relevance, enhancing video categorization, and tracking user interactions at scale. The objective was to deliver personalization quality comparable to leading platforms such as Netflix and YouTube.
the solution
Forgeahead designed and implemented a scalable recommendation engine to deliver personalized fitness content by analyzing user behavior, content attributes, and interaction patterns.
A user-based collaborative filtering approach was used to generate recommendations by identifying similarities between users and their viewing behavior, enabling the system to surface relevant workout content. In parallel, content-based filtering leveraged video metadata such as workout descriptions, intensity levels, categories, and equipment requirements to recommend similar content aligned with user preferences.
To enhance contextual discovery, pattern-based recommendations were introduced using the Apriori algorithm to identify frequently co-watched workout pairs, improving the relevance of suggested content combinations. Feature-based personalization further strengthened the system by using cosine similarity and vectorized representations to recommend workouts based on fitness focus areas, categories, and equipment usage patterns.
To ensure accuracy and performance, the recommendation system was evaluated using precision, recall, and F1-score, enabling continuous optimization of recommendation quality and relevance.
The Impact
35% Increase in User Engagement
Improved interaction with personalized and relevant content recommendations.
20 Minute Increase in Session Duration
Users spent more time exploring curated workout content.
20% Improvement in User Retention
Stronger personalization led to increased platform loyalty.
40% Expansion in Content Exploration
Broader discovery of workout categories and fitness programs.
Role of AWS
AWS enabled scalable, real-time personalization and efficient recommendation delivery across the platform. Amazon Personalize powered the core recommendation engine by processing user behavior and content interactions to generate relevant workout suggestions at scale.
AWS Lambda supported serverless processing for data ingestion and near real-time updates to recommendation outputs, ensuring low-latency and event-driven execution. Amazon DynamoDB stored user activity, content metadata, and recommendation results, enabling fast retrieval and scalable data handling for personalization workflows.
Tech Stack
The platform frontend was built using Angular, while the backend was developed in Python. Machine learning models were implemented using TensorFlow and PyTorch to support recommendation logic and personalization algorithms.
Cloud services included Amazon Personalize for recommendation capabilities, AWS Lambda for serverless processing, and Amazon DynamoDB for scalable data storage and real-time interaction tracking.