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|a 1048107078
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|a 1175622855
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|a 9781788992534
|q (electronic bk.)
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|a 1788993756
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|a (OCoLC)1050169818
|z (OCoLC)1048107078
|z (OCoLC)1048800324
|z (OCoLC)1175622855
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|a CL0500000988
|b Safari Books Online
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|a BA8F954A-6CEE-4169-94FC-82EDDAC36657
|b OverDrive, Inc.
|n http://www.overdrive.com
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4 |
|a QA76.73.P98
|b .B365 2018
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Banik, Rounak,
|e author.
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| 245 |
1 |
0 |
|a Hands-on recommendation systems with Python :
|b start building powerful and personalized, recommendation engines with Python /
|c Rounak Banik.
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| 264 |
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1 |
|a Birmingham, UK :
|b Packt Publishing,
|c 2018.
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| 300 |
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|a 1 online resource (xiii, 130 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 500 |
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|a Includes index.
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| 588 |
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|a Description based on online resource; title from title page (Safari, viewed August 27, 2018).
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0 |
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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 |
8 |
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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 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 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 |
8 |
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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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| 520 |
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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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| 650 |
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0 |
|a Python (Computer program language)
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| 650 |
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0 |
|a Internet marketing.
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| 650 |
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|a Data mining.
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| 650 |
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7 |
|a Data capture & analysis.
|2 bicssc
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| 650 |
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7 |
|a Natural language & machine translation.
|2 bicssc
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| 650 |
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7 |
|a Artificial intelligence.
|2 bicssc
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| 650 |
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7 |
|a Computers
|x Data Processing.
|2 bisacsh
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| 650 |
|
7 |
|a Computers
|x Natural Language Processing.
|2 bisacsh
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| 650 |
|
7 |
|a Computers
|x Intelligence (AI) & Semantics.
|2 bisacsh
|
| 650 |
|
7 |
|a Data mining
|2 fast
|
| 650 |
|
7 |
|a Internet marketing
|2 fast
|
| 650 |
|
7 |
|a Python (Computer program language)
|2 fast
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| 776 |
0 |
8 |
|i Print version:
|a Banik, Rounak.
|t Hands-On Recommendation Systems with Python : Start Building Powerful and Personalized, Recommendation Engines with Python.
|d Birmingham : Packt Publishing Ltd, ©2018
|z 9781788993753
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5485027
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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