Building Recommendation Systems in Python and JAX

Spis treści książki

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Warming Up
• 1. Introduction
  • Key Components of a Recommendation System
    • Collector
    • Ranker
    • Server
  • Simplest Possible Recommenders
    • The Trivial Recommender
    • Most-Popular-Item Recommender
  • A Gentle Introduction to JAX
    • Basic Types, Initialization, and Immutability
    • Indexing and Slicing
    • Broadcasting
    • Random Numbers
    • Just-in-Time Compilation
  • Summary
• 2. User-Item Ratings and Framing the Problem
  • The User-Item Matrix
  • User-User Versus Item-Item Collaborative Filtering
  • The Netflix Challenge
  • Soft Ratings
  • Data Collection and User Logging
    • What to Log
      • Page loads
      • Page views and hover
      • Clicks
      • Add-to-bag
      • Impressions
    • Collection and Instrumentation
    • Funnels
  • Business Insight and What People Like
  • Summary
• 3. Mathematical Considerations
  • Zipfs Laws in RecSys and the Matthew Effect
  • Sparsity
  • User Similarity for Collaborative Filtering
    • Pearson Correlation
    • Ratings via Similarity
  • Explore-Exploit as a Recommendation System
    • -greedy
    • What Should Be?
  • The NLP-RecSys Relationship
    • Vector Search
    • Nearest-Neighbors Search
  • Summary
• 4. System Design for Recommending
  • Online Versus Offline
  • Collector
    • Offline Collector
    • Online Collector
  • Ranker
    • Offline Ranker
    • Online Ranker
  • Server
    • Offline Server
    • Online Server
  • Summary
• 5. Putting It All Together: Content-Based Recommender
  • Revision Control Software
  • Python Build Systems
  • Random-Item Recommender
  • Obtaining the STL Dataset Images
  • Convolutional Neural Network Definition
  • Model Training in JAX, Flax, and Optax
  • Input Pipeline
  • Summary
• II. Retrieval
• 6. Data Processing
  • Hydrating Your System
    • PySpark
    • Example: User Similarity in PySpark
    • DataLoaders
    • Database Snapshots
  • Data Structures for Learning and Inference
    • Vector Search
    • Approximate Nearest Neighbors
    • Bloom Filters
    • Fun Aside: Bloom Filters as the Recommendation System
    • Feature Stores
  • Summary
• 7. Serving Models and Architectures
  • Architectures by Recommendation Structure
    • Item-to-User Recommendations
    • Query-Based Recommendations
    • Context-Based Recommendations
    • Sequence-Based Recommendations
    • Why Bother with Extra Features?
  • Encoder Architectures and Cold Starting
  • Deployment
    • Models as APIs
    • Spinning Up a Model Service
    • Workflow Orchestration
      • Containerization
      • Scheduling
      • CI/CD
  • Alerting and Monitoring
    • Schemas and Priors
    • Integration Tests
    • Observability
      • Spans and traces
      • Timeouts
  • Evaluation in Production
    • Slow Feedback
    • Model Metrics
  • Continuous Training and Deployment
    • Model Drift
    • Deployment Topologies
      • Ensembles
      • Shadowing
      • Experimentation
  • The Evaluation Flywheel
    • Daily Warm Starts
    • Lambda Architecture and Orchestration
    • Logging
      • Collector logs
      • Filtering and scoring
      • Ordering
    • Active Learning
      • Types of optimization
      • Application: User sign-up
  • Summary
• 8. Putting It All Together: Data Processing and Counting Recommender
  • Tech Stack
  • Data Representation
  • Big Data Frameworks
    • Cluster Frameworks
    • PySpark Example
  • GloVE Model Definition
    • GloVE Model Specification in JAX and Flax
    • GloVE Model Training with Optax
    • Summary
• III. Ranking
• 9. Feature-Based and Counting-Based Recommendations
  • Bilinear Factor Models (Metric Learning)
  • Feature-Based Warm Starting
  • Segmentation Models and Hybrids
    • Tag-Based Recommenders
    • Hybridization
  • Limitations of Bilinear Models
  • Counting Recommenders
    • Return to the Most-Popular-Item Recommender
    • Correlation Mining
    • Pointwise Mutual Information via Co-occurrences
    • Similarity from Co-occurrence
    • Similarity-Based Recommendations
  • Summary
• 10. Low-Rank Methods
  • Latent Spaces
  • Dot Product Similarity
  • Co-occurrence Models
  • Reducing the Rank of a Recommender Problem
    • Optimizing for MF with ALS
    • Regularization for MF
    • Regularized MF Implementation
      • Output from HPO MF
      • Prequential validation
    • WSABIE
  • Dimension Reduction
    • Isometric Embeddings
    • Nonlinear Locally Metrizable Embeddings
    • Centered Kernel Alignment
  • Affinity and p-sale
  • Propensity Weighting for Recommendation System Evaluation
    • Propensity
    • Simpsons and Mitigating Confounding
  • Summary
• 11. Personalized Recommendation Metrics
  • Environments
    • Online and Offline
    • User Versus Item Metrics
    • A/B Testing
  • Recall and Precision
    • @ k
    • Precision at k
    • Recall at k
    • R-precision
  • mAP, MMR, NDCG
    • mAP
    • MRR
    • NDCG
    • mAP Versus NDCG?
    • Correlation Coefficients
  • RMSE from Affinity
  • Integral Forms: AUC and cAUC
    • Recommendation Probabilities to AUC-ROC
    • Comparison to Other Metrics
  • BPR
  • Summary
• 12. Training for Ranking
  • Where Does Ranking Fit in Recommender Systems?
  • Learning to Rank
  • Training an LTR Model
    • Classification for Ranking
    • Regression for Ranking
    • Classification and Regression for Ranking
  • WARP
  • k-order Statistic
  • BM25
  • Multimodal Retrieval
  • Summary
• 13. Putting It All Together: Experimenting and Ranking
  • Experimentation Tips
    • Keep It Simple
    • Debug Print Statements
    • Defer Optimization
    • Keep Track of Changes
    • Use Feature Engineering
    • Understand Metrics Versus Business Metrics
    • Perform Rapid Iteration
  • Spotify Million Playlist Dataset
    • Building URI Dictionaries
    • Building the Training Data
    • Reading the Input
    • Modeling the Problem
    • Framing the Loss Function
  • Exercises
  • Summary
• IV. Serving
• 14. Business Logic
  • Hard Ranking
  • Learned Avoids
  • Hand-Tuned Weights
  • Inventory Health
  • Implementing Avoids
  • Model-Based Avoids
  • Summary
• 15. Bias in Recommendation Systems
  • Diversification of Recommendations
    • Improving Diversity
    • Applying Portfolio Optimization
  • Multiobjective Functions
  • Predicate Pushdown
  • Fairness
  • Summary
• 16. Acceleration Structures
  • Sharding
  • Locality Sensitive Hashing
  • k-d Trees
  • Hierarchical k-means
  • Cheaper Retrieval Methods
  • Summary
• V. The Future of Recs
• 17. Sequential Recommenders
  • Markov Chains
    • Order-Two Markov Chain
    • Other Markov Models
  • RNN and CNN Architectures
  • Attention Architectures
    • Self-Attentive Sequential Recommendation
    • BERT4Rec
    • Recency Sampling
    • Merging Static and Sequential
  • Summary
• 18. Whats Next for Recs?
  • Multimodal Recommendations
  • Graph-Based Recommenders
    • Neural Message Passing
    • Applications
      • Modeling user-item interactions
      • Feature learning
      • Cold-start problem
      • Context-aware recommendations
    • Random Walks
    • Metapath and Heterogeneity
  • LLM Applications
    • LLM Recommenders
    • LLM Training
    • Instruct Tuning for Recommendations
    • LLM Rankers
    • Recommendations for AI
  • Summary
• Index

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