Recommender Algorithms in 2026: A Practitioner's Guide

Rauf Aliev

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.

Table of Contents

• Abstract
• Introduction
• Structure and Prerequisites
  • How This Book Is Organized
  • Essential Mathematical Basics for Understanding Latent Matrix Models
    • Matrix Representation in Recommendations
    • Vectors and Dot Products
    • Matrix Factorization (Decomposition)
    • Loss Functions and Optimization
    • Regularization
• 1. Foundational and Heuristic-Driven Algorithms
  • 1.1. Content-Based Filtering
    • 1.1.1. The Vector Space Model (VSM): A Foundational Framework
      • Mathematical Foundations of VSM
    • 1.1.2. TF-IDF: A Classic VSM Weighting Scheme
      • Mathematical Foundations
    • 1.1.3. Embedding-based Similarity: A Latent Approach to VSM
      • Tutorial #1: Content-Based Similarity with Feature Embeddings
    • 1.1.4. Limitations of Content-Based Filtering
    • 1.1.5. Alternative Word2Vec Architecture: CBOW
    • 1.1.6. Handling Rare and Unknown Features: Subword Embeddings (FastText)
  • 1.2. Rule-Based and Heuristic Systems
    • 1.2.1. Classic Rule-Based Systems
    • 1.2.2. Heuristic-Driven Algorithms
      • 1.2.2.1. Top Popular (Most Popular)
    • 1.2.3. Association Rule Mining
      • 1.2.3.1. Frequent Itemset Mining Algorithms
      • 1.2.3.2. Rule Generation and Key Metrics
      • 1.2.3.3. Practical Considerations
      • 1.2.3.4. Use Cases & Production Readiness
• 2. Interaction-Driven Recommendation Algorithms
  • 2.1. Core Properties of Recommendation Data
    • 2.1.1. Types of Ratings Feedback
    • 2.1.2. The Long-Tail Property
  • 2.2. Recommendation Tasks
  • 2.3. Classic & Neighborhood-Based Models
    • 2.3.1. ItemKNN (Item-based k-Nearest Neighbors)
      • Mathematical Foundations
      • Tutorial: Item-based k-NN From Scratch
    • 2.3.2. SAR (Smart Adaptive Recommendations)
      • Mathematical Foundations
      • Tutorial: SAR (Smart Adaptive Recommendations) From Scratch
    • 2.3.3. UserKNN (User-based k-Nearest Neighbors)
      • Mathematical Foundations
      • Tutorial: User-based k-NN From Scratch
    • 2.3.4. Comparing User-Based and Item-Based Approaches
    • 2.3.5. SlopeOne
      • Mathematical Foundations
    • 2.3.6. Attribute-Aware k-Nearest Neighbors
  • 2.4. Latent Factor Models (Matrix Factorization)
    • 2.4.1. Classic Singular Value Decomposition (SVD)
      • Five WHYs
    • 2.4.2. FunkSVD (Biased Matrix Factorization)
      • Mathematical Foundations
      • Five WHYs
    • 2.4.3. Probabilistic Matrix Factorization (PMF)
      • Mathematical Foundations
    • 2.4.4. Weighted Regularized Matrix Factorization (WRMF)
      • Mathematical Foundations
      • Tutorial: WRMF from Scratch
    • 2.4.5. Pairwise Ranking Objective: BPR (Bayesian Personalized Ranking)
    • 2.4.6. SVD++
      • Mathematical Foundations
      • Tutorial: SVD++ (Biased Matrix Factorization) From Scratch
    • 2.4.7. TimeSVD++ (Time-aware SVD++)
      • Mathematical Foundations
    • 2.4.8. Item-based Latent Models: SLIM & FISM
      • Mathematical Foundations of SLIM
      • Tutorial: SLIM for Top-N Recommendation From Scratch
    • 2.4.9. Non-Negative Matrix Factorization (NonNegMF)
      • Mathematical Foundations
    • 2.4.10. Collaborative Metric Learning (CML)
      • Mathematical Foundations
  • 2.5. Deep Learning Hybrids & Representation Learning
    • 2.5.1. Neural Collaborative Filtering (NCF) Framework and NeuMF
      • Neural Matrix Factorization (NeuMF): The Fused Model
      • Mathematical Foundations of NeuMF
    • 2.5.2. Factorization Machine-based: DeepFM & xDeepFM
      • Mathematical Foundations
    • 2.5.3. Autoencoder-based: DAE & VAE
      • Mathematical Foundations
    • 2.5.4. A Modern Baseline for CF: SimpleX
      • Mathematical Foundations
    • 2.5.5. EASE (Embarrassingly Shallow Autoencoders)
      • Mathematical Foundations
  • 2.6. Sequential & Session-Based Models
    • 2.6.1. RNN-based: GRU4Rec
      • Mathematical Foundations
    • 2.6.2. CNN-based: NextItNet
      • Mathematical Foundations
    • 2.6.3. Attention/Transformer-based: SASRec & BERT4Rec
      • Mathematical Foundations
    • 2.6.4. With Contrastive Learning: CL4SRec
      • Mathematical Foundations
    • 2.6.5. Generative Adversarial Networks (GANs): IRGAN
      • Mathematical Foundations
    • 2.6.6. Diffusion for CF: DiffRec
      • Mathematical Foundations
    • 2.6.7. GFlowNets: GFN4Rec
      • Mathematical Foundations
    • 2.6.8. Normalizing Flows: IDNP
      • Mathematical Foundations
• 3. Context-Aware Recommendation Algorithms
  • 3.1. Factorization Machine Family
    • 3.1.1. Factorization Machines (FM)
      • Mathematical Foundations
    • 3.1.2. AMF (Attentional Factorization Machine)
  • 3.2. Cross-Network Models
    • 3.2.1. Wide & Deep
    • 3.2.2. Mathematical & Engineering Foundations of Wide & Deep
      • Mathematical Foundations
      • Engineering Pipeline
  • 3.3. Tree-Based Models for CTR Prediction
    • 3.3.1. LightGBM (Light Gradient Boosting Machine)
      • Mathematical Foundations
      • Tutorial: LightGBM for CTR Prediction
• 4. Text-Driven Recommendation Algorithms
  • 4.1. Review-Based Models
    • 4.1.1. DeepCoNN (Deep Cooperative Neural Networks)
    • 4.1.2. NARRE (Neural Attentional Rating Regression with Review-level Explanations)
  • 4.2. Large Language Model (LLM)-Based Paradigms
    • 4.2.1. Retrieval-based: Dense Retrieval & Cross-Encoders
    • 4.2.2. Generative / Instruction-Tuned
    • 4.2.3. RAG & Feature Extraction
    • 4.2.4. LLM Agents & Tool Use
  • 4.3. Conversational Recommender Systems
    • 4.3.1. Dialogue-based Preference Elicitation
    • 4.3.2. Natural Language Explanation & Critique
• 5. Multimodal Recommendation Algorithms
  • 5.1. Contrastive Learning for Multimodal Alignment
    • 5.1.1. CLIP (Contrastive Language-Image Pre-Training)
    • 5.1.2. ALBEF (Align Before Fuse)
  • 5.2. Generative Multimodal Models
    • 5.2.1. Multimodal VAEs
    • 5.2.2. Multimodal Diffusion
• 6. Knowledge-Aware Recommendation Algorithms
  • 6.1. Graph Neural Networks for CF: NGCF & LightGCN
    • 6.1.1. NGCF (Neural Graph Collaborative Filtering)
    • 6.1.2. LightGCN (Light Graph Convolution Network)
      • Mathematical Foundations
    • 6.1.3. Self-Supervised Graph Learning for CF: SGL
      • Mathematical Foundations
      • Production Ready & Frameworks
  • 6.2. Embedding-based & Path-based Models
• 7. Specialized Recommendation Tasks
  • 7.1. Debiasing & Fairness
  • 7.2. Cross-Domain Recommendation
  • 7.3. Meta-Learning for Cold Start
• 8. New Algorithmic Paradigms in Recommender Systems
  • 8.1. Reinforcement Learning (RL) for RecSys
    • 8.1.1. Core Idea
    • 8.1.2. Key Concepts & Algorithms
      • 8.1.2.1. Multi-Armed Bandits (MAB) & Contextual Bandits
      • 8.1.2.2. Deep Reinforcement Learning (e.g., DQN, Actor-Critic)
  • 8.2. Causal Inference in Recommender Systems
    • 8.2.1. Core Idea
    • 8.2.2. Key Concepts & Algorithms
      • 8.2.2.1. Uplift Modeling
    • 8.2.3. Mathematical Foundations
  • 8.3. Explainable AI (XAI) for RecSys
    • 8.3.1. Core Idea
    • 8.3.2. Key Concepts & Algorithms
      • 8.3.2.1. Graph-Based Explanations
      • 8.3.2.2. Feature-Based Explanations
• 9. Evaluating Recommender Systems
  • 9.1. Metrics for Rating Prediction
    • 9.1.1. Root Mean Squared Error (RMSE)
    • 9.1.2. Mean Absolute Error (MAE)
  • 9.2. Metrics for Top-N Ranking
    • 9.2.1. Precision@k and Recall@k
    • 9.2.2. Mean Average Precision (MAP@k)
    • 9.2.3. Normalized Discounted Cumulative Gain (nDCG@k)
  • 9.3. Beyond-Accuracy Metrics
    • 9.3.1. Diversity
  • 9.4. Metrics for Classification Tasks in Recommendation
    • 9.4.1. Confusion Matrix
    • 9.4.2. Matthews Correlation Coefficient (MCC)
    • 9.4.3. Log Loss (Binary Cross-Entropy)
    • 9.4.4. ROC Curve and AUC Score
    • 9.4.5. F-beta Score
    • 9.4.6. Cohen’s Kappa
    • 9.4.7. Brier Score
• Conclusion