万本电子书0元读

万本电子书0元读

顶部广告

Building a Recommendation Engine with Scala电子书

售       价:¥

45人正在读 | 0人评论 9.8

作       者:Saleem Ansari

出  版  社:Packt Publishing

出版时间:2016-01-05

字       数:69.4万

所属分类: 进口书 > 外文原版书 > 电脑/网络

温馨提示:此类商品不支持退换货,不支持下载打印

为你推荐

  • 读书简介
  • 目录
  • 累计评论(0条)
  • 读书简介
  • 目录
  • 累计评论(0条)
Learn to use Scala to build a recommendation engine from scratch and empower your website usersAbout This BookLearn the basics of a recommendation engine and its application in e-commerceDiscover the tools and machine learning methods required to build a recommendation engineExplore 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 LearnDiscover the tools in the Scala ecosystemUnderstand the challenges faced in e-commerce systems and learn how you can solve those challenges with a recommendation engineFamiliarise yourself with machine learning algorithms provided by the Apache Spark frameworkBuild different versions of recommendation engines from practical code examplesEnhance the user experience by learning from user feedbackDive 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.
目录展开

Building a Recommendation Engine with Scala

Table of Contents

Building a Recommendation Engine with Scala

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

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

2. Data Processing Pipeline Using Scala

Entree – a sample dataset for recommendation systems

Data analysis of the Entree dataset

ETL – extract transform load

Extract

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

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 methods

Frequently 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

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 transformation

Term 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

5. Recommendation Engines and Where They Fit in?

Populating the Amazon dataset

Creating a web app with user/product pages

Creating a Play framework application

The home page

Product Groups

Product view

Customer views

Adding recommendation pages

The Top Rated view

The Most Popular view

The Monthly Trends view

Summary

6. Collaborative Filtering versus Content-Based Recommendation Engines

Content-based recommendation

Similarity measures

Pearson correlation

Challenges with Pearson correlation

Euclidean distance

Challenges with 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?

ALS in Apache Spark

ALS on Amazon ratings

Content-based versus collaborative filtering

Summary

7. Enhancing the User Experience

Adding product search

Setting up Elasticsearch

Adding recommendation listings

Understanding recommendation behavior

Why is that so?

Logging

Ranking

Diversification

Justification

Evaluation

Summary

8. Learning from User Feedback

Introducing PredictionIO

Installing PredictionIO

Unified recommender

Summary

Index

累计评论(0条) 0个书友正在讨论这本书 发表评论

发表评论

发表评论,分享你的想法吧!

买过这本书的人还买过

读了这本书的人还在读

回顶部