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

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