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用机器学习和人工智能建立推荐系统:用深度学习、神经网络和机器学习推荐帮助人们发现新产品和新内容-Building Recommender Systems with Machine Learning a文件编号:276

上传于 2020年2月25日 16:02
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标题(title):Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Machine Learning Recommendations
作者(author):Frank Kane
出版社(publisher):Independently Published
大小(size):48 MB (50260114 bytes)

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

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