Recommender System with Machine Learning and Artificial Intelligence - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Recommender System with Machine Learning and Artificial Intelligence write by Sachi Nandan Mohanty. This book was released on 2020-07-08. Recommender System with Machine Learning and Artificial Intelligence available in PDF, EPUB and Kindle. This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
Building Recommender Systems with Machine Learning and AI.
Building Recommender Systems with Machine Learning and AI. - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Building Recommender Systems with Machine Learning and AI. write by Frank Kane. This book was released on 2018. Building Recommender Systems with Machine Learning and AI. available in PDF, EPUB and Kindle. Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.
Personalization Techniques and Recommender Systems
Personalization Techniques and Recommender Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Personalization Techniques and Recommender Systems write by Gulden Uchyigit. This book was released on 2008. Personalization Techniques and Recommender Systems available in PDF, EPUB and Kindle. The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed.The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems.This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.
Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications
Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications write by Abhishek Majumder. This book was released on 2023-08-16. Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications available in PDF, EPUB and Kindle. Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: · Basic application of machine learning and deep learning in recommendation process and the evaluation metrics · Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 · Tensor factorization in different types of recommendation system · Ranking framework and topic modeling to recommend author specialization based on content. · Movie recommendation systems · Point of interest recommendations · Mobile tourism recommendation systems for visually disabled persons · Automation of fashion retail outlets · Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.
Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach
Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach write by Frank Kane. This book was released on 2018-08-11. Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach available in PDF, EPUB and Kindle. 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 recommendersThis 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!
