BOOK's WEBINAR: Deep Recommender Systems with Python (Saturday 16 & Sunday 17 November 2024)

Indicative Educational Material (Access to my book and its supplementary material (slides, python code, etc.) will only be given to registered participants)

You can register in the 2-day weekend Webinar on November 2024 using the link below:
Book's Webinar from the University of the Aegean      



The slides of my Tutorial@RecSys2024 can be found in the link below:

Deep Recommendation using Graphs (SLIDES)





 

Chapter 1. Introduction in Recommender Systems.pdf


1.1 Objectives of a Recommendation System

1.2 Recommendation Systems Types

1.3 Recommendation Algorithm Types

1.4 Chapter Questions

Chapter1.pptx

 

Chapter2. Collaborative Filtering Algorithms.pdf


2.1 Introduction in CF algorithms

2.2 Factors Affecting the CF Process

2.3 Comparative Performance of CF algorithms

2.4 Chapter Questions

2.5 Python Exercise: Constructing a User-User Similarity Matrix

Chapter2.pptx

Python code in Google Colab

 

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

Python code in Google Colab

 

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

Python code in Google Colab

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

Python code in Google Colab

Chapter 6. Deep Neural Nets and Genetic Algorihtms.pdf


6.1 Perceptron's structure

6.2 Single-layer Perceptron

6.3 Multi-layer Perceptron

6.4 Convolutional Neural Networks

6.5 Recurrent Neural Networks

6.6 Genetic Algorithms

6.7 Genetic Operations

6.8 Python Exercise: Use a Neural Network to Factorise a User-Item Rating Matrix

Slides (draft)

Python code in Google Colab


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

(Book Example 8.5) Python code in Google Colab

(Book Example 8.10) Code for a Graph Convolution Neural Network

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

Python code in Google Colab

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


Intro in Python