Recommender Systems Course

This is the repository to support the course in Recommender Systems

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📚 Recommender Systems Course - Lecture Breakdown

This directory contains materials for each lecture in the Recommender Systems course. The course consists of 8 lectures, each 3 hours long, covering the fundamentals, evaluation metrics, classical approaches, hybrid models, and modern deep learning techniques.

📖 Course Lectures

1️⃣ Introduction to Recommender Systems

📌 Overview of recommender systems and their real-world applications
📌 Types of recommender systems (content-based, collaborative filtering, hybrid)
📌 Business impact and challenges in recommendation
📌 Datasets: MovieLens, Netflix Prize, Amazon Reviews
📌 Hands-on: Setting up a simple rule-based recommender

📂 Materials: Lecture1_Intro/

2️⃣ Evaluation Metrics for Recommender Systems

📌 Offline vs. online evaluation
📌 Accuracy metrics: Precision, Recall, MAP, NDCG
📌 Error-based metrics: RMSE, MAE
📌 Beyond accuracy: diversity, novelty, serendipity, coverage
📌 A/B testing and online evaluation
📌 Hands-on: Implementing evaluation metrics in Python

📂 Materials: Lecture2_Evaluation/

3️⃣ Content-Based Filtering

📌 How content-based filtering works
📌 Feature extraction: TF-IDF, embeddings for text, images, audio
📌 Cosine similarity and nearest neighbours
📌 Strengths and weaknesses of content-based methods
📌 Hands-on: Implementing a TF-IDF-based movie recommender

📂 Materials: Lecture3_ContentBased/

4️⃣ Collaborative Filtering

📌 User-based vs. item-based collaborative filtering
📌 Pearson correlation and similarity measures
📌 Model-based methods: Matrix factorisation (SVD, ALS)
📌 Cold start and sparsity issues
📌 Hands-on: Implementing collaborative filtering with user-item data

📂 Materials: Lecture4_CollaborativeFiltering/

5️⃣ Hybrid Recommender Systems

📌 Why hybrid models?
📌 Strategies: weighted, switching, feature combination
📌 Netflix Prize: How Netflix optimised recommendations
📌 Hands-on: Implementing a hybrid recommender

📂 Materials: Lecture5_Hybrid/

6️⃣ LensKit Library for Recommender Systems

📌 Introduction to LensKit and its advantages
📌 Implementing content-based and collaborative filtering using LensKit
📌 Evaluating recommendation models with LensKit
📌 Hands-on: Training and evaluating models in LensKit

📂 Materials: Lecture6_LensKit/

7️⃣ Matrix Factorisation & Advanced Techniques

📌 Matrix factorisation fundamentals: SVD, ALS
📌 Factorisation Machines and Bayesian Personalised Ranking
📌 Implicit feedback handling
📌 Hands-on: Implementing matrix factorisation with Surprise

📂 Materials: Lecture7_MatrixFactorization/

8️⃣ Deep Neural Networks for Recommender Systems

📌 Neural Collaborative Filtering (NCF)
📌 Autoencoders and Variational Autoencoders (VAEs)
📌 RNNs for sequential recommendations
📌 Transformers in recommender systems
📌 Hands-on: Building a deep learning-based recommender

📂 Materials: Lecture8_DeepLearning/

🎯 Final Project

📌 Build and evaluate a custom recommender system
📌 Apply concepts from previous lectures
📌 Present results and insights

📂 Materials: FinalProject/

📚 References

This course is built upon a mix of theoretical foundations, industry best practices, and hands-on implementations from various sources. Below are key references used throughout the lectures:

📖 Books

  • Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems Handbook. Springer.
  • Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer.
  • Okura, K., Tagami, Y., Ono, S., & Tajima, A. (2017). Deep Neural Networks for YouTube Recommendations. arXiv.

📄 Academic Papers

  • Resnick, P., & Varian, H. R. (1997). Recommender Systems. Communications of the ACM, 40(3), 56-58.
  • Sarwar, B. M., Karypis, G., Konstan, J. A., & Riedl, J. (2001). Item-Based Collaborative Filtering Recommendation Algorithms. WWW Conference.
  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural Collaborative Filtering. WWW Conference.
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. IEEE Computer.

🔨 Libraries & Tools

  • LensKit - A Python toolkit for building and experimenting with recommender systems.
  • Surprise - A scikit-based Python library for collaborative filtering.
  • TensorFlow Recommenders - TensorFlow-based deep learning models for recommendation tasks.

📂 Datasets

  • MovieLens - Widely used dataset for evaluating recommender systems.
  • Netflix Prize - Historic dataset from the Netflix recommendation competition.
  • Amazon Reviews - Large-scale e-commerce recommendation dataset.

🎓 Online Courses & Tutorials

📌 If you find useful references or additional resources that complement this course, feel free to contribute by adding them here!