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Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress L. P.,
2023.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro
- Table of Contents
- About the Authors
- About the Technical Reviewer
- Preface
- Chapter 1: Introduction to Recommendation Systems
- What Are Recommendation Engines?
- Recommendation System Types
- Types of Recommendation Engines
- Market Basket Analysis (Association Rule Mining)
- Content-Based Filtering
- Collaborative-Based Filtering
- Hybrid Systems
- ML Clustering
- ML Classification
- Deep Learning
- Rule-Based Recommendation Systems
- Popularity
- Global Popular Items
- Popular Items by Country
- Buy Again
- Summary
- Chapter 2: Market Basket Analysis (Association Rule Mining)
- Implementation
- Data Collection
- Importing the Data as a DataFrame (pandas)
- Cleaning the Data
- Insights from the Dataset
- Customer Insights
- Loyal Customers
- Number of Orders per Customer
- Money Spent per Customer
- Patterns Based on DateTime
- Preprocessing the Data
- How Many Orders Are Placed per Month?
- How Many Orders Are Placed per Day?
- How Many Orders Are Placed per Hour?
- Free Items and Sales
- Item Insights
- Most Sold Items Based on Quantity
- Items Bought by the Highest Number of Customers
- Most Frequently Ordered Items
- Top Ten First Choices
- Frequently Bought Together (MBA)
- Apriori Algorithm Concepts
- Association Rules
- Implementation Using mlxtend
- If A => then B
- Creating a Function
- Validation
- Visualization of Association Rules
- Summary
- Chapter 3: Content-Based Recommender Systems
- Approach
- Implementation
- Data Collection and Downloading Word Embeddings
- Importing the Data as a DataFrame (pandas)
- Preprocessing the Data
- Text to Features
- One-Hot Encoding (OHE)
- CountVectorizer
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word Embeddings
- Similarity Measures
- Euclidean Distance
- Cosine Similarity
- Manhattan Distance
- Build a Model Using CountVectorizer
- Build a Model Using TF-IDF Features
- Build a Model Using Word2vec Features
- Build a Model Using fastText Features
- Build a Model Using GloVe Features
- Build a Model Using a Co-occurrence Matrix
- Summary
- Chapter 4: Collaborative Filtering
- Implementation
- Data Collection
- About the Dataset
- Memory-Based Approach
- User-to-User Collaborative Filtering
- Implementation
- Item-to-Item Collaborative Filtering
- Implementation
- KNN-based Approach
- Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Supervised Learning
- Regression
- Classification
- K-Nearest Neighbor
- Implementation
- Summary
- Chapter 5: Collaborative Filtering Using Matrix Factorization, Singular Value Decomposition, and Co-Clustering
- Implementation
- Matrix Factorization, Co-Clustering, and SVD
- Implementing NMF
- Implementing Co-Clustering
- Implementing SVD
- Getting the Recommendations
- Summary
- Chapter 6: Hybrid Recommender Systems
- Implementation
- Data Collection
- Data Preparation