Introduction

Recommender systems are at the heart of modern digital experiences, driving personalized recommendations for users on platforms like Netflix, Amazon, Spotify, and more. This 5-day intensive training course is designed to provide participants with a deep understanding of recommender system design, from foundational algorithms to advanced techniques. Participants will learn how to build, evaluate, and deploy recommender systems that deliver personalized and impactful user experiences. The course also addresses ethical considerations and future trends, ensuring participants are prepared to tackle real-world challenges and innovate in this rapidly evolving field.

Objectives

By the end of this course, participants will:

1. Understand the fundamentals of recommender systems, including types, applications, and challenges.

2. Gain proficiency in collaborative filtering, content-based filtering, and hybrid recommendation techniques.

3. Learn advanced methods such as matrix factorization, deep learning-based recommenders, and context-aware systems.

4. Explore evaluation metrics and techniques for measuring recommender system performance.

5. Apply recommender system design principles to real-world problems in e-commerce, entertainment, and social media.

6. Understand ethical considerations, including bias, fairness, and privacy in recommender systems.

7. Explore future trends, such as explainable recommendations, reinforcement learning, and federated learning.

Who Should Attend?

This course is ideal for:

• Data scientists and machine learning engineers looking to specialize in recommender systems.

• Software developers and engineers interested in building recommendation engines.

• Product managers and business analysts seeking to leverage recommender systems for user engagement.

• Researchers and academics exploring advanced recommendation techniques.

• Professionals in e-commerce, entertainment, social media, and other industries where personalization is critical.

• AI enthusiasts and practitioners preparing for future challenges in recommender systems.

Course Outline

Day 1: Foundations of Recommender Systems

• Morning Session:

  • Introduction to Recommender Systems: Types, Applications, and Challenges

  • Overview of Recommendation Algorithms: Collaborative Filtering, Content-Based Filtering, and Hybrid Approaches

  • Data Preprocessing for Recommender Systems: Handling Sparse Data and Implicit Feedback

• Afternoon Session:

  • Hands-on Lab: Building a Simple Collaborative Filtering Model

  • Introduction to Evaluation Metrics: Precision, Recall, F1-Score, and RMSE

  • Case Study: Real-World Applications of Recommender Systems

Day 2: Collaborative Filtering and Matrix Factorization

• Morning Session:

  • User-Based and Item-Based Collaborative Filtering

  • Challenges in Collaborative Filtering: Cold Start, Scalability, and Sparsity

  • Hands-on Lab: Implementing Collaborative Filtering with Surprise Library

• Afternoon Session:

  • Introduction to Matrix Factorization: Singular Value Decomposition (SVD) and Alternating Least Squares (ALS)

  • Hands-on Lab: Building a Matrix Factorization Model

  • Case Study: Movie Recommendations Using Matrix Factorization

Day 3: Content-Based and Hybrid Recommender Systems

• Morning Session:

  • Content-Based Filtering: Feature Extraction and Similarity Metrics

  • Hands-on Lab: Building a Content-Based Recommender System

  • Hybrid Recommender Systems: Combining Collaborative and Content-Based Approaches

• Afternoon Session:

  • Hands-on Lab: Designing a Hybrid Recommender System

  • Case Study: Hybrid Recommendations in E-Commerce

  • Advanced Techniques: Knowledge-Based and Demographic Filtering

Day 4: Advanced Recommender Systems

• Morning Session:

  • Deep Learning for Recommender Systems: Neural Collaborative Filtering (NCF) and Autoencoders

  • Hands-on Lab: Building a Deep Learning-Based Recommender with TensorFlow/Keras

  • Context-Aware Recommender Systems: Incorporating Temporal, Spatial, and Social Context

• Afternoon Session:

  • Reinforcement Learning for Recommender Systems: Multi-Armed Bandits and Exploration-Exploitation Tradeoffs

  • Case Study: Personalized News Recommendations Using Context-Aware Systems

  • Ethical Considerations: Bias, Fairness, and Privacy in Recommender Systems

Day 5: Evaluation, Deployment, and Capstone Project

• Morning Session:

  • Advanced Evaluation Techniques: A/B Testing, Offline Evaluation, and Online Metrics

  • Model Interpretability and Explainable Recommendations: SHAP and LIME

  • Deploying Recommender Systems: Tools, Best Practices, and Scalability

• Afternoon Session:

  • Capstone Project: End-to-End Recommender System Design for a Real-World Problem

  • Project Presentations and Feedback

  • Course Wrap-up: Key Takeaways, Resources for Further Learning, and Certification

Key Features of the Course

• Hands-on labs using modern tools like Python, Surprise, TensorFlow, and Scikit-Learn.

• Real-world case studies and industry-relevant applications.

• Focus on ethical AI, model interpretability, and future-proofing skills.

• Access to course materials, code repositories, and a community forum for ongoing learning.

Preparing for Future Challenges

This course is designed to not only address current industry needs but also prepare participants for emerging trends and challenges in recommender systems. By focusing on ethical AI, explainability, and advanced techniques, attendees will be equipped to lead innovation and adapt to the rapidly evolving landscape of personalized recommendations.