Recommendation System for an On-Demand
Fitness Platform
Enhanced user experience and retention with personalized video suggestions
About
The Client
A global leader in on-demand fitness delivers high-quality fitness media and programming to diverse environments. Their innovative platform provides users with tailored digital fitness content that is accessible anytime and anywhere—from studio spaces to personal devices—offering the ultimate virtual fitness experience.
The
Challenge
Our client faced challenges in maintaining high user engagement and retention due to insufficient personalized recommendations. The key issues included:
Difficulty in keeping users engaged with relevant content.
Low retention rates from inadequate personalization.
Challenges in categorizing and recommending videos effectively.
Managing and processing extensive user interaction logs and video metadata.
Scaling the recommendation system to accommodate a growing user base and video library.
What Forgeahead Did
Forgeahead developed a sophisticated recommendation system using a multi-faceted approach
User-Based Filtering
Suggested videos based on the preferences of similar users.
Content-Based Filtering
Leveraged video metadata and TF-IDF vectorization for content similarity recommendations.
Frequent Watch Together Analysis
Applied the various algorithms to identify and recommend videos frequently watched together.
Tailored Content Recommendations
Recommended videos based on user interaction zones, combined with similarities in categories, foci, and equipment, for a customized viewing experience.
0%
boost in user
engagement
0+
minutes increase in time spent by users
0+
increase in retention rate
0%
more videos explored by users
0%
growth in the user base
0%
seamless increase in video library
0%
increase in trending content identification
Technology Stack
Frontend
Backend
Machine Learning Models
Cloud Services
Personalization
DynamoDB
AWS Lambda
What’s next?
Developing a Secure, Multi-Tenant SaaS Platform for Modern Learning
