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Building a recommendation engine with Scala : learn to use Scala to build a recommendation engine from scratch and empower your website users /
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 kin...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2016.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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?