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Emotive and personality parameters in recommender systems

emotions and personality that explain a substantial part of variance in the end users' preferences. The book covers the detection of emotions and;

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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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Applied Recommender Systems with Python

of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such;

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Applied Recommender Systems with Python

of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such;

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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

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning;

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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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User Modeling, Adaption, and Personalization

numerous submissions. The papers cover the following topics: recommender systems, student modeling, social media and teams, human cognition;

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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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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

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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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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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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Recommender Systems

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Active Learning for Recommender Systems

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A Practitioner's Guide to Rational-Emotive Behavior Therapy

, personality disorders, and addictions. Psychologists, clinical social workers, mental health counselors, psychotherapists, and students and trainees;

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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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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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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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AI for Cars

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

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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

and selected from 67 submissions. The papers are organized in topical sections on data mining methods; databases, information retrieval;

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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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Mobile Web and Intelligent Information Systems

: mobile web systems, recommender systems, security and authentication, context-awareness, mobile web and advanced applications, cloud and IoT;

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Advances in Databases and Information Systems

, mining, analysis and clustering; model-driven engineering, conceptual modeling; data warehouse and multidimensional modeling, recommender systems;

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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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