Recommender Systems An Introduction.pdf下载

Recommender Systems.pdf

Recommender Systems : An introduction —— Dietmar Jannach, TU Dortmund, Germany

IntroductiontoRecommenderSystemsHandbook英文原版

推荐系统入门必看书籍，分类清晰，易于理解，适合新手阅读及高手回顾

Latent Cross Making Use of Context in Recurrent.pdf

Latent Cross Making Use of Context in Recurrent.pdf

Building Recommendation Engines

Building Recommendation Engines English | 5 Jan. 2017 | ISBN: 1785884859 | 357 Pages | AZW3/MOBI/EPUB/PDF (conv) | 81.07 MB Key Features A step-by-step guide to building recommendation engines that are personalized, scalable, and real time Get to grips with the best tool available on the market to create recommender systems This hands-on guide shows you how to implement different tools for recommendation engines, and when to use which Book Description A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. The book starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, Spark, Neo4j, and Hadoop. You will get an insight into the pros and cons of each recommendation engine and when to use which recommendation to ensure each pick is the one that suits you the best. During the course of the book, you will create simple recommendation engine, real-time recommendation engine, scalable recommendation engine, and more. You will familiarize yourselves with various techniques of recommender systems such as collaborative, content-based, and cross-recommendations before getting to know the best practices of building a recommender system towards the end of the book! What you will learn Build your first recommendation engine Discover the tools needed to build recommendation engines Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations Create efficient decision-making systems that will ease your work Familiarize yourself with machine learning algorithms in different frameworks Master different versions of recommendation engines from practical code examples Explore various recommender systems and implement them in popular techniques with R, Python, Spark, and others

高校技术转移办公室如何实现科技成果的动态管理与多维分析？.docx

科易网基于AI模型+行业大数据，在技术转移、成果转化、技术经纪、知识产权、产业创新、科技招商等垂直领域的多样化应用场景，推出科技创新领域的AI+工具与数智化解决方案，推动科技创新与产业创新智能化发展。