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 / Rounak Banik.

Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies or groceries, goes a long way in defining user experience and enticing your customers to use and buy from your platfo...

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Bibliographic Details
Main Author: Banik, Rounak (Author)
Format: eBook
Language:English
Published: Birmingham, UK : Packt Publishing, 2018.
Subjects:
Python (Computer program language)
Internet marketing.
Data mining.
Data capture & analysis.
Natural language & machine translation.
Artificial intelligence.
Computers > Data Processing.
Computers > Natural Language Processing.
Computers > Intelligence (AI) & Semantics.
Data mining
Internet marketing
Online Access:Click for online access
Table of Contents:
  • 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 DataFrameThe 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 scoreBuilding 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 techniquesLinear-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 filteringMean; 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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