Personalized "For You" Page Algorithm for NixStar App
Budget: $50 – $200 USD
Looking for Developers to Build a Personalized "For You" Page Algorithm using ML
Project Overview:
I am looking for experienced developers to build a personalized "For You" page recommendation system for the NixStar Android app, similar to TikTok. The system should recommend reels to users based on their interactions, preferences, and engagement history. The implementation should use machine learning techniques, to enhance user experience.
Project Requirements:
The project consists of two main recommendation systems:
1. User interaction based Recommendations:
- The system should recommend reels based on user interactions (likes, watch history, shares, etc.).
- Should support real-time updates to adapt to user preferences dynamically.
2. Global Reels performance based Recommendations:
- Trending Content: Detect trending videos globally, which can be based on overall engagement, views, shares, or other metrics.
- High-Performance Video Identification: Identify videos that are performing exceptionally well, and push them into the recommendation stream for users.
- Diversity in Recommendations: Ensure a variety of content in the global recommendations list to avoid content saturation and offer different genres, themes, and styles.
✅ GUI/Dashboard Requirements:
Admin Dashboard:
Real-Time Monitoring: Track key metrics like user engagement and trending content.
Content Performance: View video performance (likes, views, shares).
Basic Adjustments: Enable fine-tuning of recommendation settings.
AND more metrics that are required.
Key Features:
Real-time Adaptation: The recommendation engine must update dynamically, taking into account changes in user behavior and content performance in real time, to keep learning.
Scalable Architecture: The system should be able to scale to accommodate a growing number of users and increasing content volume without compromising performance.
Machine Learning Integration: Use machine learning algorithms to analyze user interaction patterns and predict content preferences.
Backend: The backend should be a fast-serving API of recommendations to users as a JSON list.
Our current backend:
- Laravel
- MySQL
Looking for Developers Who Can:
✅ Build a robust recommendation system using ML techniques.
✅ Develop a scalable and efficient API backend.
✅ Integrate real-time data updates for dynamic recommendations.
✅ Optimize the system for performance and engagement.
If you're interested in working on this project, feel free to reach out with your experience, approach, proposal, and estimated timeline for completion.
Project Overview:
I am looking for experienced developers to build a personalized "For You" page recommendation system for the NixStar Android app, similar to TikTok. The system should recommend reels to users based on their interactions, preferences, and engagement history. The implementation should use machine learning techniques, to enhance user experience.
Project Requirements:
The project consists of two main recommendation systems:
1. User interaction based Recommendations:
- The system should recommend reels based on user interactions (likes, watch history, shares, etc.).
- Should support real-time updates to adapt to user preferences dynamically.
2. Global Reels performance based Recommendations:
- Trending Content: Detect trending videos globally, which can be based on overall engagement, views, shares, or other metrics.
- High-Performance Video Identification: Identify videos that are performing exceptionally well, and push them into the recommendation stream for users.
- Diversity in Recommendations: Ensure a variety of content in the global recommendations list to avoid content saturation and offer different genres, themes, and styles.
✅ GUI/Dashboard Requirements:
Admin Dashboard:
Real-Time Monitoring: Track key metrics like user engagement and trending content.
Content Performance: View video performance (likes, views, shares).
Basic Adjustments: Enable fine-tuning of recommendation settings.
AND more metrics that are required.
Key Features:
Real-time Adaptation: The recommendation engine must update dynamically, taking into account changes in user behavior and content performance in real time, to keep learning.
Scalable Architecture: The system should be able to scale to accommodate a growing number of users and increasing content volume without compromising performance.
Machine Learning Integration: Use machine learning algorithms to analyze user interaction patterns and predict content preferences.
Backend: The backend should be a fast-serving API of recommendations to users as a JSON list.
Our current backend:
- Laravel
- MySQL
Looking for Developers Who Can:
✅ Build a robust recommendation system using ML techniques.
✅ Develop a scalable and efficient API backend.
✅ Integrate real-time data updates for dynamic recommendations.
✅ Optimize the system for performance and engagement.
If you're interested in working on this project, feel free to reach out with your experience, approach, proposal, and estimated timeline for completion.