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Chapter 1. Introduction in Recommender Systems.pdf
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Chapter2. Collaborative Filtering Algorithms.pdf
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Chapter3. Content-based Filtering Algorithms.pdf 3.1 Introduction in CBF algorithms 3.2 The Vector Space Model 3.3 Recommendations based on Content Data 3.4 Decision Tree Classifier 3.5 Feature Selection 3.6 Naive Bayes Classifier 3.7 Python Exercise: Recommend Friends based on the CBF Algorithm Chapter3.ppt |
Chapter4. Context-aware Recommender Systems.pdf 4.1 Time-aware Recommendation Systems 4.2 Location-aware Recommendation Systems 4.3 Recommendation systems for LBSNs 4.4 Types of Recommendations in LBSNs 4.5 Hybrid and Ensemble Methods 4.6 Types of Hybrid Systems 4.7 Python Exercise: Recommend Friends based on a Hybrid Algorithm Chapter4.pptx |
Chapter 5. Matrix Decomposition Algorithms.pdf
5.1 Eigenvalue Decomposition 5.2 Singular Value Decomposition 5.3 From SVD to UV-decomposition 5.4 Tensor Decomposition 5.5 From SVD to UV-decomposition 5.6 Tensor Decomposition 5.7 Tucker Decomposition 5.8 Python Exercise: Apply UV-decomposition to a User-Item Rating Matrix Chapter 5.pptx |
Chapter 7. Deep Reinforcement Learning in Recommender Systems.pdf 7.1 Q-learning Algorithm 7.2 Step-by-Step Execusion of the Q-learning Algorithm 7.3 Deep Reinforcement Learning 7.4 Deep Q-Network with Experience Replay Algorithm 7.5 Advantage Actor Critic Algorithm 7.6 Python Exercise 1: Implement the Tabular Q-learning Algorithm 7.7 Python Exercise 2: Implement the DQN Algorithm Chapter7.pptx Python code in Google Colab |
Chapter 8. Deep Graph Neural Networks in Recommender Systems.pdf 8.1 Graphs Fundamentals 8.2 Local-based Similarity Measures 8.3 Global-based Similarity Measures 8.4 Knowledge Graphs 8.5 Graph Convolutional Networks 8.6 Python Exercise: Graph-based Recommendations for an Online Newspaper chapter 8.pptx
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Chapter 9. Εvaluation Metrics.pdf 9.1 Introduction to Models' Evaluation 9.2 MAE and RMSE 9.3 Precision, Recall and F1 metric 9.4 ROC curve and AUC metric 9.5 Normalized Discounted Cumulative Gain 9.6 Beyond Accuracy Metrics 9.7 Python Exercise: Build an Evaluation Framework chapter9.pptx
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Chapter 10. New Trends in Recommender Systems.pdf 10.1 Group Recommender Systems 10.2 Ethics in Recommender Systems 10.3 Fairness, Accountability, Censorship 10.4 Privacy and Recommendation Systems 10.5 Systems' Architecture for Privacy 10.6 Algorithmic Techniques for Privacy Protection 10.7 Legal Framework for Privacy Protection Chapter10.pptx |
