(Ebook) Building Recommendation Systems in Python and JAX 1st Edition by Bryan Bischof, Hector Yee ISBN 1492097993 9781492097990

(Ebook) Building Recommendation Systems in Python and JAX 1st Edition by Bryan Bischof, Hector Yee ISBN 1492097993 9781492097990

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ISBN 10: 1492097993
ISBN 13: 9781492097990
Author: Bryan Bischof, Hector Yee

(Ebook) Building Recommendation Systems in Python and JAX 1st Edition Table of contents:

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

Collection and Instrumentation

Funnels

Business Insight and What People Like

Summary

3. Mathematical Considerations

Zipf’s Laws in RecSys and the Matthew Effect

Sparsity

User Similarity for Collaborative Filtering

Pearson Correlation

Ratings via Similarity

Explore-Exploit as a Recommendation System

ϵ -greedy

What Should ϵ 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

Alerting and Monitoring

Schemas and Priors

Integration Tests

Observability

Evaluation in Production

Slow Feedback

Model Metrics

Continuous Training and Deployment

Model Drift

Deployment Topologies

The Evaluation Flywheel

Daily Warm Starts

Lambda Architecture and Orchestration

Logging

Active Learning

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

WSABIE

Dimension Reduction

Isometric Embeddings

Nonlinear Locally Metrizable Embeddings

Centered Kernel Alignment

Affinity and p-sale

Propensity Weighting for Recommendation System Evaluation

Propensity

Simpson’s 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. What’s Next for Recs?

Multimodal Recommendations

Graph-Based Recommenders

Neural Message Passing

Applications

Random Walks

Metapath and Heterogeneity

LLM Applications

LLM Recommenders

LLM Training

Instruct Tuning for Recommendations

LLM Rankers

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Tags: Bryan Bischof, Hector Yee, Recommendation Systems, JAX