Building Recommender Systems with Machine Learning and AI: Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

Frank Kane

  • 出版商: Independently published
  • 出版日期: 2018-08-11
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 510
  • 裝訂: Paperback
  • ISBN: 1718120125
  • ISBN-13: 9781718120129
  • 相關分類: 推薦系統Machine LearningDeepLearning
  • 立即出貨(限量) (庫存=2)



Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them. This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you'll have access to all of the source code associated with it as well. We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data. This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover: -Building a recommendation engine -Evaluating recommender systems -Content-based filtering using item attributes -Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF -Model-based methods including matrix factorization and SVD -Applying deep learning, AI, and artificial neural networks to recommendations -Session-based recommendations with recursive neural networks -Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines -Real-world challenges and solutions with recommender systems -Case studies from YouTube and Netflix -Building hybrid, ensemble recommenders This comprehensive book takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user. The coding exercises for this book use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this book successfully. We also include a short introduction to deep learning, Tensorfow, and Keras if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms. Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is!


從亞馬遜的先驅之一學習如何建立推薦系統。Frank Kane在亞馬遜工作了九年多,負責管理和領導了許多亞馬遜的個性化產品推薦技術的開發。你在Netflix的主頁、YouTube和亞馬遜上都看到了自動推薦,這些機器學習算法會了解你的獨特興趣,並為你作為個體展示最佳產品或內容。這些技術已成為最大、最有聲望的科技雇主的核心,通過了解它們的工作原理,你將對他們非常有價值。本書改編自Frank在Sundog Education發布的熱門在線課程,因此你可以期待從幻燈片中獲得大量的視覺輔助材料,並且整本書都以對話式、易於理解的語氣呈現。書中包含了超過300張幻燈片的圖形和腳本,你還可以獲得與之相關的所有源代碼。我們將介紹基於鄰域協同過濾的經過驗證的推薦算法,並逐步介紹更現代的技術,包括矩陣分解和甚至使用人工神經網絡的深度學習。在此過程中,你將從Frank的豐富行業經驗中學習,以了解在大規模和實際數據應用這些算法時會遇到的現實挑戰。本書非常實用;你將開發自己的框架來評估和結合多種不同的推薦算法,甚至使用Tensorflow構建自己的神經網絡,從真實人們的電影評分中生成推薦。我們將涵蓋以下內容: -建立推薦引擎 -評估推薦系統 -使用項目屬性的基於內容的過濾 -基於鄰域的協同過濾,包括基於用戶、基於項目和KNN CF -基於模型的方法,包括矩陣分解和SVD -應用深度學習、人工智能和人工神經網絡於推薦 -使用遞歸神經網絡進行基於會話的推薦 -使用Apache Spark機器學習、亞馬遜DSSTNE深度學習和AWS SageMaker與分解機械應對大規模數據集 -推薦系統的現實挑戰和解決方案 -YouTube和Netflix的案例研究 -構建混合、集成推薦系統 這本全面的書將帶你從協同過濾的早期發展,到深度神經網絡和現代機器學習技術的前沿應用,為每個個體用戶推薦最佳項目。本書的編程練習使用Python編程語言。如果你對Python不熟悉,我們會提供一個簡介,但你需要有一些先前的編程經驗才能成功使用本書。如果你對人工智能領域不熟悉,我們還會提供一個簡短的深度學習、Tensorflow和Keras介紹,但你需要能夠理解新的計算機算法。開始吧,學習機器學習和深度學習中最有趣和有利可圖的應用之一!