Tutorial Index#
Comprehensive tutorials for all CoreRec models.
Getting Started#
Before diving into specific models, we recommend:
Read the Installation Guide
Follow the QuickStart
Understand Core Concepts
Production Models (Tested & Stable)#
These models are production-ready — fully tested, CI-enforced, and implement the complete BaseRecommender interface. Start here.
Core Engine Models#
Collaborative Filtering Models#
Content-Based Models#
Sandbox Models (Experimental)#
Sandbox Notice
The models below are experimental. They are included for research and learning purposes. Sandbox models may have incomplete implementations and are not covered by production CI tests. See Model Tiers for details.
Each sandbox tutorial explains the model’s architecture, mathematical foundations, use cases, and scaling considerations. Code examples are provided as reference implementations — they may require additional validation before production use.
Neural Network Models (Sandbox)#
- AFM Tutorial: Attentional Factorization Machine
- AutoFI Tutorial: Automatic Feature Interaction
- AutoInt Tutorial: Automatic Feature Interaction Learning via Self-Attention
- BST Tutorial: Behavior Sequence Transformer
- BiVAE Tutorial: Bilateral Variational Autoencoder
- Caser Tutorial: Convolutional Sequence Embedding Recommendation
- DCN_base Tutorial: Deep & Cross Network Base
- DeepCrossing Tutorial: Deep Crossing Network
- DeepFM_base Tutorial: DeepFM Base
- DeepRec Tutorial: Deep Recommender
- DIEN Tutorial: Deep Interest Evolution Network
- DIFM Tutorial: Dual Input Factorization Machine
- DIN Tutorial: Deep Interest Network
- DLRM Tutorial: Deep Learning Recommendation Model
- ENSFM Tutorial: Ensemble Factorization Machine
- ESCMM Tutorial: Entire Space Cross Multi-Task Model
- ESMM Tutorial: Entire Space Multi-Task Model
- FGCNN Tutorial: Feature Generation with CNN
- FFM Tutorial: Field-aware Factorization Machine
- Fibinet Tutorial: Feature Importance and Bilinear feature Interaction
- FLEN Tutorial: Feature-aware Local Encoding Network
- FM Tutorial: Factorization Machine
- GAN Tutorial: Generative Adversarial Network
- GateNet Tutorial: Gating Network
- GNN_base Tutorial: Graph Neural Network Base
- GRU-CF Tutorial: GRU for Collaborative Filtering
- NFM Tutorial: Neural Factorization Machine
- NextItNet Tutorial: Next Item Net
- WideDeep Tutorial: Wide & Deep Learning
- YouTubeDNN Tutorial: YouTube Deep Neural Network
- PNN Tutorial: Product-based Neural Network
- MMoE Tutorial: Multi-gate Mixture-of-Experts
- PLE Tutorial: Progressive Layered Extraction
- TDM Tutorial: Tree-based Deep Model
Matrix Factorization Models (Sandbox)#
- A2SVD Tutorial: Adaptive Singular Value Decomposition
- ALS Tutorial: Alternating Least Squares
- FM_Base Tutorial: Factorization Machine Base
- MatrixFactorization Tutorial: Matrix Factorization
- MF_Base Tutorial: MF Base
- SVD Tutorial: Singular Value Decomposition
- UserBased Tutorial: User-Based Collaborative Filtering
Graph-Based Models (Sandbox)#
Sequential Models (Sandbox)#
Bayesian Models (Sandbox)#
Content-Based Models (Sandbox)#
Pipeline & System Tutorials#
End-to-end system tutorials:
Tutorial Structure#
Production Model Tutorials#
Full working examples with tested code you can copy-paste and run.
Sandbox Model Tutorials#
Each sandbox tutorial covers:
Introduction — What the model does and its original paper
Architecture & Theory — How it works, with diagrams and math
Reference Implementation — Code examples (informational, not production-tested)
Use Cases & Scaling — When to use it, how to scale, and production considerations
Key Takeaways — Best practices and further reading
Learning Path#
Beginners#
Start with DCN Tutorial (Production)
Explore SAR Tutorial (Production)
Intermediate#
Deep dive into DeepFM (Production)
Learn Graph Methods with LightGCN (Production)
Advanced#
Explore Sandbox Models for research
Deploy to Production