This book serves as an essential practitioner's guide to the world of recommender algorithms as it stands in early 2026. We begin with the indispensable baselines—from classic neighborhood models to powerful matrix factorization—and build toward the sophisticated deep learning architectures that power today's largest platforms, including hybrids for CTR prediction and state-of-the-art sequential models.
A core theme of this guide is the practical integration of the latest technological breakthroughs. We dedicate significant attention to the transformative impact of Large Language Models (LLMs), offering architectural blueprints for leveraging them as powerful semantic feature extractors, building reliable Retrieval-Augmented Generation (RAG) pipelines, and designing the next wave of generative and conversational recommender agents. Furthermore, we explore the critical role of multimodal models like CLIP for solving visual cold-start problems and provide insights into specialized areas like debiasing and fairness.
This is more than a survey; it is a toolkit for the modern engineer. Each section balances conceptual depth with pragmatic advice on implementation, scalability, and production readiness, making it the definitive resource for professionals tasked with creating value through personalization.
Foundational and Heuristic-Driven Algorithms- Vector Space Model (VSM)
- TF-IDF
- Embedding-based Similarity (Word2Vec)
- CBOW (Continuous Bag-of-Words)
- FastText
- Classic Rule-Based Systems
- Top Popular
- Apriori / FP-Growth / Eclat
Interaction-Driven Recommendation Algorithms- ItemKNN / UserKNN
- SAR
- SlopeOne
- Attribute-Aware k-NN
- FunkSVD
- PMF
- WRMF
- BPR
- SVD++
- TimeSVD++
- SLIM & FISM
- Non-Negative Matrix Factorization (NonNegMF)
- CML
- NCF & NeuMF
- DeepFM & xDeepFM
- Autoencoder-based (DAE & VAE)
- SimpleX
- EASE
- GRU4Rec
- NextItNet
- SASRec & BERT4Rec
- CL4SRec
- TBGRecall
- IRGAN
- DiffRec
- GFN4Rec
- IDNP (Interest Dynamics Neural Process)
- WMFBPR (Weighted MF + BPR)
- ASVD (Asymmetric SVD)
- SKNN (Session-Based KNN)
Text-Driven Recommendation AlgorithmsMultimodal Recommendation Algorithms- CLIP
- ALBEF (Align Before Fuse)
Context-Aware Recommendation Algorithms- Factorization Machines (FM)
- AMF (Attentional Factorization Machine)
- Wide & Deep
- GBDT
- XGBoos
- LightGBM
- DCN
Knowledge-Aware Recommendation Algorithms- NGCF
- LightGCN
- SGL
- Embedding-based (CKE, KTUP)
- Path-based (RippleNet)
- GNN-based (KGCN, KGAT, KGIN)
Specialized Recommendation Tasks- MF-IPS
- CausE
- FairRec
- CMF
- CoNet
- MeLU
New Algorithmic Paradigms- Reinforcement Learning (RL) for RecSys
- Causal Inference in RecSys
- Inverse Propensity Scoring (IPS)
- Doubly Robust (DR) Methods
- Uplift Modeling
- SCM-Based Debiasing (PDA, DecRS, IV4Rec)
- Counterfactuals (CauseRec, PSF-RS, CountER)
- Explainable AI (XAI) for RecSys
- Fairness-Aware RecSys
- Diversity and Novelty Optimization (MMR)
Please be aware that the depth of explanation varies across different algorithms. Foundational concepts may be covered in greater detail, while others are presented more concisely.
Complimentary app: https://github.com/raliev/recommender-algorithms
Complimentary app (deployed): https://recommender-algorithms.streamlit.app/
