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|a 9781484289549
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|a ZA3084
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|a Kulkarni, Akshay
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|a Applied Recommender Systems with Python
|b Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques
|c Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan
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| 260 |
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|a Berkeley, CA
|b Apress L. P.
|c 2023
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| 300 |
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|a xiii, 248 pages
|b illustrations
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| 505 |
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|a 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
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| 505 |
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|a 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
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|a 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
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|a 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
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|a 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)
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|a Réseaux neuronaux (Informatique)
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|a Neural networks (Computer science) / fast
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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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 Artificial intelligence / fast
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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|a Recommender systems (Information filtering) / fast
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|a Systèmes de recommandation (Filtrage d'information)
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|a Recommender systems (Information filtering) / http://id.loc.gov/authorities/subjects/sh2007003098
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|a Intelligence artificielle
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|a Neural networks (Computer science) / http://id.loc.gov/authorities/subjects/sh90001937
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|a Machine learning / fast
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|a Apprentissage automatique
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|a artificial intelligence / aat
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| 653 |
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|a Python (Langage de programmation)
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| 700 |
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|a Shivananda, Adarsha
|e author
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| 700 |
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|a Kulkarni, Anoosh
|e author
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| 700 |
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|a Krishnan, V. Adithya
|e author
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| 041 |
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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 Description based upon print version of record
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| 024 |
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|a 10.1007/978-1-4842-8954-9
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| 776 |
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|z 9781484289549
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| 776 |
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|z 9781484289532
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| 776 |
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|z 1484289536
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|z 1484289544
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|u https://learning.oreilly.com/library/view/~/9781484289549/?ar
|x Verlag
|3 Volltext
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|a 331
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|a 500
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|a 025.04
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|a 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 of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is For Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems
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