Trying to Build 'Recommendations System' to an e-commerce site using Reinforcement Learning

[Dipanjan10]

Hello everyone i want to bould a 'Recommendations System using Reinforcement Learning but i am facing some challenges where to start how to deploy the whole thing, can you suggest some videos or a course that i can follow.

[nceniza]

Recommendations System seems to be broad to me, Personally I would Explore Vertex AI first as this is Google's Machine Learning platform, and then narrow down what feature or API  should be using to the said use case: 

Overview: https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform

Vid Introduction: https://www.youtube.com/watch?v=-3Olw-C4FN4&list=PLIivdWyY5sqLRCzKJyixrIDPQKwU6XHpn&t=1s

[Rupankar]

Building a recommendation system using reinforcement learning (RL) is an advanced and exciting project. It involves understanding both the fundamentals of recommendation systems and the principles of reinforcement learning. Here are some resources, including courses and videos, to help you get started and guide you through the process:

Understanding Recommendation Systems

1. Coursera - "Recommender Systems Specialization" by University of Minnesota

   • A comprehensive introduction to recommendation systems.
   • Link to Course

2. Udemy - "Building Recommender Systems with Machine Learning and AI"

   • Practical approach to building recommendation systems.
   • Link to Course

Learning Reinforcement Learning

1. Coursera - "Reinforcement Learning Specialization" by University of Alberta & DeepMind

   • A detailed course on the fundamentals of reinforcement learning.
   • Link to Course

2. DeepMind x UCL - Deep Reinforcement Learning Lecture Series

   • Free YouTube lecture series by DeepMind and UCL.
   • Link to Series

Combining RL with Recommendation Systems

1. YouTube - "Reinforcement Learning for Recommendation Systems" by TensorFlow

   • A practical guide on how to apply RL in recommendation systems.
   • Link to Video

2. arXiv Papers and Tutorials

   • Research papers on arXiv can be insightful for understanding the latest advancements in RL-based recommendation systems.
   • Search for "Reinforcement Learning in Recommendation Systems" on arXiv.

Tools and Frameworks

• Learn to use frameworks like TensorFlow, PyTorch, and specialized libraries for RL (like OpenAI Gym, Ray RLLib).

Deployment and Practical Aspects

1. Udemy - "Deployment of Machine Learning Models"

   • For understanding deployment aspects.
   • Link to Course

2. AWS / Azure / GCP Documentation and Tutorials

   • These cloud platforms offer services and tutorials for deploying ML models.

Community and Further Learning

• GitHub Repositories: Search for projects on GitHub related to RL-based recommendation systems to understand real-world codebases.
• Meetups and Conferences: Attend AI/ML conferences, webinars, or local meetups.
• Kaggle: Engage with the Kaggle community for practical insights and competitions.