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|a 9781788992534
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|a 1788992539
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|a 1788993756
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|a QA76.73.P98
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|a Banik, Rounak
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|a Hands-on recommendation systems with Python
|b start building powerful and personalized, recommendation engines with Python
|c Rounak Banik
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| 260 |
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|a Birmingham, UK
|b Packt Publishing
|c 2018
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| 300 |
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|a xiii, 130 pages
|b illustrations
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|a 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
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| 505 |
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|a 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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|a 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
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| 505 |
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|a 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
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| 505 |
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|a 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
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|a Internet marketing / http://id.loc.gov/authorities/subjects/sh95005028
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|a Data mining / fast
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|a Artificial intelligence / bicssc
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|a Internet marketing / fast
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|a Python (Computer program language) / fast
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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|a Computers / Natural Language Processing / bisacsh
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|a Marketing sur Internet
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|a Computers / Intelligence (AI) & Semantics / bisacsh
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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|a Natural language & machine translation / bicssc
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|a Python (Langage de programmation)
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|a Data capture & analysis / bicssc
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|a Exploration de données (Informatique)
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Includes index
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| 776 |
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|z 1788993756
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|z 9781788992534
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|u https://learning.oreilly.com/library/view/~/9781788993753/?ar
|x Verlag
|3 Volltext
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|a 005.133
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|a 381.142
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|a 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 platform. This book teaches you to do just that
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