Hands-On Recommendation Systems with Python: Start building powerful and personalized, recommendation engines with Python

Rounak Banik

  • 出版商: Packt Publishing
  • 出版日期: 2018-07-27
  • 售價: $1,050
  • 貴賓價: 9.5$998
  • 語言: 英文
  • 頁數: 146
  • 裝訂: Paperback
  • ISBN: 1788993756
  • ISBN-13: 9781788993753
  • 相關分類: Python程式語言推薦系統
  • 立即出貨 (庫存=1)

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商品描述

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web

Key Features

  • Build industry-standard recommender systems
  • Only familiarity with Python is required
  • No need to wade through complicated machine learning theory to use this book

Book Description

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix 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 your platform.

This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible..

In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques 

With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.

What you will learn

  • Get to grips with the different kinds of recommender systems
  • Master data-wrangling techniques using the pandas library
  • Building an IMDB Top 250 Clone
  • Build a content based engine to recommend movies based on movie metadata
  • Employ data-mining techniques used in building recommenders
  • Build industry-standard collaborative filters using powerful algorithms
  • Building Hybrid Recommenders that incorporate content based and collaborative fltering

Who this book is for

If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Table of Contents

  1. Getting Started with Recommender Systems
  2. Manipulating Data with the Pandas Library
  3. Building an IMDB Top 250 Clone with Pandas
  4. Building Content-Based Recommenders
  5. Getting Started with Data Mining Techniques
  6. Building Collaborative Filters
  7. Hybrid Recommenders

商品描述(中文翻譯)

透過《使用Python進行實作的推薦系統》,學習建立各種強大推薦系統(協同過濾、知識和基於內容)所需的工具和技術,並將其部署到網路上。

主要特點:
- 建立符合業界標準的推薦系統
- 只需要熟悉Python即可
- 無需深入研究複雜的機器學習理論即可使用本書

書籍描述:
推薦系統幾乎是當今互聯網業務的核心,從Facebook到Netflix到Amazon,提供良好的推薦,無論是朋友、電影還是雜貨,都能大大影響用戶體驗並吸引顧客使用您的平台。

本書將向您展示如何實現這一目標。您將學習業界使用的不同類型的推薦系統,並了解如何使用Python從頭開始構建它們。無需深入研究大量的機器學習理論,您將盡快開始構建和學習推薦系統。

在本書中,您將建立一個IMDB Top 250的克隆版本,這是一個基於電影元數據的基於內容的引擎。您將使用協同過濾來利用客戶行為數據,並使用混合推薦器結合基於內容和協同過濾技術。

通過本書,您只需要熟悉Python就可以開始建立推薦系統,並且在完成時,您將對推薦系統的運作有很好的理解,並能夠將所學的技術應用於自己的問題領域。

您將學到:
- 瞭解不同類型的推薦系統
- 掌握使用pandas庫進行數據整理的技巧
- 使用pandas構建IMDB Top 250克隆版本
- 建立基於內容的推薦引擎,根據電影元數據推薦電影
- 使用在構建推薦系統中使用的數據挖掘技術
- 使用強大的算法構建符合業界標準的協同過濾器
- 構建結合基於內容和協同過濾的混合推薦器

本書適合對社交網絡、新聞個性化或智能廣告開發應用程序的Python開發人員。對機器學習技術有基本了解將有所幫助,但不是必需的。

目錄:
1. 開始使用推薦系統
2. 使用Pandas庫操作數據
3. 使用Pandas構建IMDB Top 250克隆版本
4. 構建基於內容的推薦系統
5. 開始使用數據挖掘技術
6. 構建協同過濾器
7. 混合推薦器