Hands-on recommendation systems with Python : start building powerful and personalized, recommendation engines with Python

Hands-on recommendation systems with Python start building powerful and personalized, recommendation engines with Python

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity...

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Detalles Bibliográficos
Otros Autores: Banik, Rounak, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham ; Mumbai : Packt 2018.
Edición:1st edition
Materias:
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630745806719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Recommender Systems
  • Technical requirements
  • What is a recommender system?
  • The prediction problem
  • The ranking problem
  • Types of recommender systems
  • Collaborative filtering
  • User-based filtering
  • Item-based filtering
  • Shortcomings
  • Content-based systems
  • Knowledge-based recommenders
  • Hybrid recommenders
  • Summary
  • Chapter 2: Manipulating Data with the Pandas Library
  • Technical requirements
  • Setting up the environment
  • The Pandas library
  • The Pandas DataFrame
  • The Pandas Series
  • Summary
  • Chapter 3: Building an IMDB Top 250 Clone with Pandas
  • Technical requirements
  • The simple recommender
  • The metric
  • The prerequisties
  • Calculating the score
  • Sorting and output
  • The knowledge-based recommender
  • Genres
  • The build_chart function
  • Summary
  • Chapter 4: Building Content-Based Recommenders
  • Technical requirements
  • Exporting the clean DataFrame
  • Document vectors
  • CountVectorizer
  • TF-IDFVectorizer
  • The cosine similarity score
  • Plot description-based recommender
  • Preparing the data
  • Creating the TF-IDF matrix
  • Computing the cosine similarity score
  • Building the recommender function
  • Metadata-based recommender
  • Preparing the data
  • The keywords and credits datasets
  • Wrangling keywords, cast, and crew
  • Creating the metadata soup
  • Generating the recommendations
  • Suggestions for improvements
  • Summary
  • Chapter 5: Getting Started with Data Mining Techniques
  • Problem statement
  • Similarity measures
  • Euclidean distance
  • Pearson correlation
  • Cosine similarity
  • Clustering
  • k-means clustering
  • Choosing k
  • Other clustering algorithms
  • Dimensionality reduction
  • Principal component analysis.
  • Other dimensionality reduction techniques
  • Linear-discriminant analysis
  • Singular value decomposition
  • Supervised learning
  • k-nearest neighbors
  • Classification
  • Regression
  • Support vector machines
  • Decision trees
  • Ensembling
  • Bagging and random forests
  • Boosting
  • Evaluation metrics
  • Accuracy
  • Root mean square error
  • Binary classification metrics
  • Precision
  • Recall
  • F1 score
  • Summary
  • Chapter 6: Building Collaborative Filters
  • Technical requirements
  • The framework
  • The MovieLens dataset
  • Downloading the dataset
  • Exploring the data
  • Training and test data
  • Evaluation
  • User-based collaborative filtering
  • Mean
  • Weighted mean
  • User demographics
  • Item-based collaborative filtering
  • Model-based approaches
  • Clustering
  • Supervised learning and dimensionality reduction
  • Singular-value decomposition
  • Summary
  • Chapter 7: Hybrid Recommenders
  • Technical requirements
  • Introduction
  • Case study - Building a hybrid model
  • Summary
  • Other Books You May Enjoy
  • Index.

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