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Recommender Systems for Medicine and Music

Music recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media;

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Applied Recommender Systems with Python

learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python;

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Applied Recommender Systems with Python

recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building;

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Practical Recommender Systems

Description Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors;

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Recommender Systems for Learning

of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or;

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Recommender Systems

robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better;

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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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Foundations of Intelligent Systems

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

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Active Learning for Recommender Systems

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Recommender Systems

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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Recommender Systems

, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems;

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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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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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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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Personalization Techniques And Recommender Systems

learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.;

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Emotive and personality parameters in recommender systems

of a recommender system for images. The book will serve as a guideline and inspiration for practitioners and academics in content retrieval and;

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Recommender Systems

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AI for Cars

AI for Cars book provides a short-guided tour through many different AI landscapes including robotics, image and speech processing;

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Building a Recommendation System with R

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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Theory and Applications of Formal Argumentation

This book constitutes the refereed proceedings of the 4th International Workshop on the Theory and Applications of Formal Argumentation;

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Towards Metadata-Aware Algorithms for Recommender Systems

problem has always been of great interest for both academic and industry. Metadata such as content information about the items (attributes) have;

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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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Statistical Methods for Recommender Systems

challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling;

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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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Computational Collective Intelligence

from 204 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender;

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Mobile Web and Intelligent Information Systems

were carefully reviewed and selected from 77 submissions. The call for papers of the MobiWis 2017 included new and emerging areas such as;

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Towards Analytical Techniques for Systems Engineering Applications

., systems for cattle management), and medical and social-related systems (e.g., recommender systems).;

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