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Computer Science
Artificial Intelligence
Machine Learning
Recommender Systems
1. Introduction to Recommender Systems
2. Data Foundations and Preprocessing
3. Content-Based Filtering
4. Collaborative Filtering
5. Hybrid Recommender Systems
6. Advanced Recommender Models
7. Evaluation of Recommender Systems
8. Practical Challenges and System Design
Advanced Recommender Models
Context-Aware Recommender Systems
Defining Context
Temporal Context
Spatial Context
Social Context
Mood and Activity Context
Device Context
Paradigms for Incorporating Context
Contextual Pre-filtering
Contextual Post-filtering
Contextual Modeling
Context Representation
Contextual Features
Context Vectors
Deep Learning for Recommendation
Neural Network Foundations
Multi-Layer Perceptrons
Embedding Layers
Activation Functions
Deep Matrix Factorization
Neural Collaborative Filtering
Deep Factorization Machines
Autoencoders for Recommendation
Denoising Autoencoders
Variational Autoencoders
Recurrent Neural Networks for Sequential Recommendation
LSTM Networks
GRU Networks
Sequence Modeling
Graph Neural Networks for Recommendation
Graph Convolutional Networks
Graph Attention Networks
Modeling User-Item Graphs
Convolutional Neural Networks
CNN for Text and Image Features
Feature Learning
Factorization Machines
Modeling Feature Interactions
Second-Order Interactions
Applications in Sparse Data
Field-Aware Factorization Machines
Learning to Rank
Pointwise Approaches
Regression-Based Methods
Pairwise Approaches
Ranking SVM
RankNet
Listwise Approaches
ListNet
AdaRank
Loss Functions for Ranking
Hinge Loss
Cross-Entropy Loss
Reinforcement Learning for Recommendation
Multi-Armed Bandits
Contextual Bandits
Deep Reinforcement Learning
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5. Hybrid Recommender Systems
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7. Evaluation of Recommender Systems