Personalized Content Rows in Connected Fitness Platforms

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Solution Overview

Problem

Users of connected fitness platforms face difficulty in finding personalized and engaging content among a vast array of exercise classes, instructors, and music, leading to an overwhelming experience.

Innovation Solution

The implementation of a recommendation system that uses matrix factorization and collaborative filtering to identify similar instructors and musical artists based on user interactions, surfacing personalized rows of exercise classes and music recommendations on the user interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a vast array of exercise classes, instructors, and music are provided on the fitness platform, then content variety and selection are improved, but user experience becomes overwhelming and difficult to navigate

Engineering Contradiction:

Improvecontent varietyVSAvoiduser experience

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system collects user interaction data (views, selections, completions) and uses it to continuously refine personalized recommendations through collaborative filtering, creating a feedback loop that adapts to user preferences over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system provides different content presentations to different users based on their individual preferences and behaviors, with each user seeing a customized arrangement of classes, instructors, and music tailored to their specific interests

Inventive Principle:

Principle #3Local quality

2Ease of operation

If personalized recommendations are implemented using matrix factorization and collaborative filtering, then user engagement is enhanced, but system complexity increases

Engineering Contradiction:

Improveuser engagementVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system introduces a recommendation engine as an intermediary layer between the user and the content library, using matrix factorization to compute latent factor matrices that mediate the matching process between user preferences and available content

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual content selection with an automated collaborative filtering algorithm that computationally determines recommendations based on user behavior patterns, substituting mechanical user navigation with intelligent system-driven curation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20240325856A1User experience personalization in a connected fitness platform

Publication Date: 2024.10.03 PELOTON INTERACTIVE INC

AI summary

The systems and methods described herein facilitate an enhanced user experience within a connected fitness system. For example, the systems and methods can enhance and/or personalize a homescreen experience and interface for a user of a connected fitness platform. The homescreen experience, when personalized, can provide a user with enhanced or specifically tailored rows of content, such as recommended classes or activities, for selection by the user.