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Practical Recommender Systems

. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender;

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Applied Recommender Systems with Python

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an;

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Applied Recommender Systems with Python

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an;

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Collaborative Recommendations: Algorithms, Practical Challenges And Applications

Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e;

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Recommender Systems for Learning

learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an;

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Recommender Systems

Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a;

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Recommender Systems

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Recommender System for Online Social Network

recommender systems, the relation between these research areas is still unclear. An innovative idea, which enables to integrate these areas, and;

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Active Learning for Recommender Systems

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Introduction to Data Science

parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis;

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Recommender Systems in Fashion and Retail

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

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to;

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Recommender Systems

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to;

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Personalization Techniques And Recommender Systems

knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing;

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Social Network-Based Recommender Systems

analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working;

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Building a Recommendation System with R

optimize the recommendation algorithms * Prepare and structure the data before building models * Discover different recommender systems along;

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Towards Metadata-Aware Algorithms for Recommender Systems

As recommender systems (RS) allow means of guiding consumers through the overloaded choices of products available, the recommendation;

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Emotive and personality parameters in recommender systems

The growing amount of multimedia content is making it hard for end users to find the relevant content. The goal of recommender systems;

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AI for Cars

, recommender systems and on to deep learning, all within the automobile world.;

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Towards Analytical Techniques for Systems Engineering Applications

interested in using these techniques for solving specific practical problems. For many real-world, complex systems, it is possible to create easy-to;

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Towards Analytical Techniques for Systems Engineering Applications

interested in using these techniques for solving specific practical problems. For many real-world, complex systems, it is possible to create easy-to;

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User Modeling, Adaptation, and Personalization

; recommender systems (including topics such as matrix factorization, critiquing, noise and spam in recommender systems); user centered design and;

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Statistical Methods for Recommender Systems

and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth;

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Foundations of Intelligent Systems

, recommender systems; machine learning; knowledge representation, semantic web; emotion recognition, music information retrieval; network analysis;

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Guide to High Performance Distributed Computing

systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Features: describes the;

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Human-Computer Interaction

usability engineering, cognitive architectures, spoken dialogue systems and recommender systems are covered. New dimensions of HCI, such as;

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Internet Computing and Big Data

Applications Security + Communication Systems + Recommender Systems + High Performance Computing Social Networks + Blogs + Text Messaging Systems and;

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