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learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python;
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recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building;
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of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or;
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of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to;
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This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to;
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robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better;
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This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), and it aims to present a;
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, recommender systems and on to deep learning, all within the automobile world.;
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Description Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors;
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Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;
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field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and;
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applies recommendation systems to the online social network systems, is presented in this book. Recommender systems for social networks differ;
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build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning;
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statistical methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design;
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methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS;
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methods; recommender systems; and transfer learning. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS;
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, recommender systems; machine learning; knowledge representation, semantic web; emotion recognition, music information retrieval; network analysis;
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topical sections: blockchain and crowdsourcing; machine learning; deep learning; recommender systems, data mining; web-based applications; entity;
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: data mining and machine learning; deep learning and applications for industry 4.0; recommender systems; computer vision techniques; decision;
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Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e;
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, technology opportunity, e-learning, and fuzzy intelligent systems; analysis for image, video and motion data in life sciences; real world;
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, technology opportunity, e-learning, and fuzzy intelligent systems; analysis for image, video and motion data in life sciences; real world;
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, reasoning, and logic; intelligent systems and environment; intelligent Web and recommender systems; machine learning and data mining; metaheuristics;
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analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working;
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applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques;
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