Building a recommendation engine with Scala : (Record no. 49982)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200827111837.0 |
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| fixed length control field | 160122s2016 enka o 001 0 eng d |
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| Language of cataloging | eng |
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| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781785282980 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1785282980 |
| Qualifying information | (electronic bk.) |
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| Qualifying information | (Trade Paper) |
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| 037 ## - SOURCE OF ACQUISITION | |
| Stock number | CL0500000706 |
| Source of stock number/acquisition | Safari Books Online |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.73.S28 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 051010 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.114 |
| Edition number | 23 |
| 049 ## - LOCAL HOLDINGS (OCLC) | |
| Holding library | MAIN |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ansari, Saleem, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Building a recommendation engine with Scala : |
| Remainder of title | learn to use Scala to build a recommendation engine from scratch and empower your website users / |
| Statement of responsibility, etc. | Saleem Ansari. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Birmingham, UK : |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2016. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource : |
| Other physical details | illustrations. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
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| 337 ## - MEDIA TYPE | |
| Media type term | computer |
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| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
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| 490 1# - SERIES STATEMENT | |
| Series statement | Community experience distilled |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Online resource; title from cover page (Safari, viewed January 21, 2016). |
| 500 ## - GENERAL NOTE | |
| General note | Includes index. |
| 520 8# - SUMMARY, ETC. | |
| Summary, etc. | Learn to use Scala to build a recommendation engine from scratch and empower your website usersAbout This Book Learn the basics of a recommendation engine and its application in e-commerce Discover the tools and machine learning methods required to build a recommendation engine Explore different kinds of recommendation engines using Scala libraries such as MLib and SparkWho This Book Is ForThis book is written for those who want to learn the different tools in the Scala ecosystem to build a recommendation engine. No prior knowledge of Scala or recommendation engines is assumed. What You Will Learn Discover the tools in the Scala ecosystem Understand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engine Familiarise yourself with machine learning algorithms provided by the Apache Spark framework Build different versions of recommendation engines from practical code examples Enhance the user experience by learning from user feedback Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendationsIn DetailWith an increase of data in online e-commerce systems, the challenges in assisting users with narrowing down their search have grown dramatically. The various tools available in the Scala ecosystem enable developers to build a processing pipeline to meet those challenges and create a recommendation system to accelerate business growth and leverage brand advocacy for your clients. This book provides you with the Scala knowledge you need to build a recommendation engine. You'll be introduced to Scala and other related tools to set the stage for the project and familiarise yourself with the different stages in the data processing pipeline, including at which stages you can leverage the power of Scala and related tools. You'll also discover different machine learning algorithms using MLLib. As the book progresses, you will gain detailed knowledge of what constitutes a collaborative filtering based recommendation and explore different methods to improve users' recommendation. Style and approachA step-by-step guide full of real-world, hands-on examples of Scala recommendation engines. Each example is placed in context with explanation and visuals. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Introduction to Scala and Machine Learning; Setting up Scala, SBT, and Apache Spark; A quick introduction to Scala; Case classes; Tuples; Scala REPL; SBT -- Scala Build Tool; Apache Spark; Setting up a standalone Apache Spark cluster; Apache Spark -- MLLib; Machine learning and recommendation engines; Summary; Chapter 2: Data Processing Pipeline Using Scala; Entree -- a sample dataset for recommendation systems; Data analysis of the Entree dataset |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | ETL -- extract transform loadExtract; Transform; Load; Extraction and transformation for machine learning; Types of data; Discrete; Continuous; Categorical; Cleaning the data; Missing data; Normalization; Standardization; Setting up MongoDB and Apache Kafka; Setting up MongoDB; Setting up Apache Kafka; Data processing pipeline for Entree; How does it relate to information retrieval?; Summary; Chapter 3: Conceptualizing an E-Commerce Store; Importance of recommender systems in e-commerce; Converting browsers into buyers; Making cross-sell happen; Increased loyalty time |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Types of recommendation methodsFrequently bought together; An example of frequent patterns; People to people correlation; Customer reviews and ratings; People who were also interested in other similar items; Recommendation from others' views; Example of similar items; Manual; Automatic; Ephemeral; Persistent; The architecture of the project; Batch versus online; Summary; Chapter 4: Machine Learning Algorithms; Hands on with Spark/MLlib; Data types; Vector; Matrix; Labeled point; Statistics; Summary statistics; Correlation; Sampling; Hypothesis testing; Random data generation |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Feature extraction and transformationTerm frequency-inverted document frequency (TF-IDF); Word2Vec; StandardScaler; Normalizer; Feature selection; Dimensionality reduction; Classification/regression; Linear methods; Naive Bayes; Decision trees; Ensembles; Clustering; K-Means; Expectation-maximization; Power iteration clustering; Latent Dirichlet Allocation; LDA example; Association analysis; Frequent pattern mining (FPGrowth); Summary; Chapter 5: Recommendation Engines and Where They Fit in?; Populating the Amazon dataset; Creating a web app with user/product pages |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Creating a Play framework applicationThe home page; Product Groups; Product view; Customer views; Adding recommendation pages; The Top Rated view; The Most Popular view; The Monthly Trends view; Summary; Chapter 6: Collaborative Filtering versus Content-Based Recommendation Engines; Content-based recommendation; Similarity measures; Pearson correlation; Euclidean distance; Cosine measure; Spearman correlation; Tanimoto coefficient; Log likelihood test; Content-based recommendation steps; Clustering for performance; Collaborative filtering based recommendation; What is ALS? |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | eBooks on EBSCOhost |
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| Topical term or geographic name entry element | Scala (Computer program language) |
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| Topical term or geographic name entry element | Machine learning. |
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| Relationship information | Erscheint auch als: |
| Note | Druck-Ausgabe |
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| Uniform title | Community experience distilled. |
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