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Author: Ying Qin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"The development of the Internet and the emergence of audio compression technologies have contributed to the realization of making millions of music titles accessible to millions of users. Due to the extensive distribution of music, consumers are being presented with a problem of information overload, while the music industry is being faced with the challenge of personalized promotion and distribution. Music recommendation systems aim to ease the task of finding the music items that might interest the users by generating meaningful recommendations. The recommendation for music is different from those for books and movies, due to its low cost per item, short consumption time, high per-item reuse, highly contextual usage, and numerous item types. Understanding the patterns of music listening and consumption is important to create accurate and satisfying music recommendations. This thesis reviews state-of-the-art music recommendation and discovery methods with the goal of presenting the historical developments in this area. Traditional music recommendation systems can be classied as one of two major kinds: collaborative filtering and content-based filtering. Recently, the research community has broadened its attention to include other aspects, such as hybrid approaches, context awareness, social tagging, music networks, visualization, playlist generation, and group recommendation. For the evaluation of music recommendation systems, researchers or developers need to take into account properties such as accuracy, coverage, confidence, novelty, diversity, and privacy. These properties can be measured in an offline simulation, a user study, or an online evaluation. Suggestions for future work in both the design and the evaluation of music recommendation systems are given." --
Author: Ying Qin
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"The development of the Internet and the emergence of audio compression technologies have contributed to the realization of making millions of music titles accessible to millions of users. Due to the extensive distribution of music, consumers are being presented with a problem of information overload, while the music industry is being faced with the challenge of personalized promotion and distribution. Music recommendation systems aim to ease the task of finding the music items that might interest the users by generating meaningful recommendations. The recommendation for music is different from those for books and movies, due to its low cost per item, short consumption time, high per-item reuse, highly contextual usage, and numerous item types. Understanding the patterns of music listening and consumption is important to create accurate and satisfying music recommendations. This thesis reviews state-of-the-art music recommendation and discovery methods with the goal of presenting the historical developments in this area. Traditional music recommendation systems can be classied as one of two major kinds: collaborative filtering and content-based filtering. Recently, the research community has broadened its attention to include other aspects, such as hybrid approaches, context awareness, social tagging, music networks, visualization, playlist generation, and group recommendation. For the evaluation of music recommendation systems, researchers or developers need to take into account properties such as accuracy, coverage, confidence, novelty, diversity, and privacy. These properties can be measured in an offline simulation, a user study, or an online evaluation. Suggestions for future work in both the design and the evaluation of music recommendation systems are given." --
Author: Jyotsna Kumar Mandal
Publisher: Springer
ISBN: 9811374031
Category : Technology & Engineering
Languages : en
Pages : 799
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Book Description
The book covers cutting-edge and advanced research in modelling and graphics. Gathering high-quality papers presented at the First International Conference on Emerging Technology in Modelling and Graphics, held from 6 to 8 September 2018 in Kolkata, India, it addresses topics including: image processing and analysis, image segmentation, digital geometry for computer imaging, image and security, biometrics, video processing, medical imaging, and virtual and augmented reality.
Author: Zbigniew W. Ras
Publisher: Springer Nature
ISBN: 3030664503
Category : Technology & Engineering
Languages : en
Pages : 236
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Book Description
Music recommendation systems are becoming more and more popular. The increasing amount of personal data left by users on social media contributes to more accurate inference of the user’s musical preferences and the same to quality of personalized systems. Health recommendation systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of valuable information at the right time by ensuring information quality, trustworthiness, authentication, and privacy concerns. Medical doctors deal with various kinds of diseases in which the music therapy helps to improve symptoms. Listening to music may improve heart rate, respiratory rate, and blood pressure in people with heart disease. Sound healing therapy uses aspects of music to improve physical and emotional health and well-being. The book presents a variety of approaches useful to create recommendation systems in healthcare, music, and in music therapy.
Author: Shlomo Berkovsky
Publisher: World Scientific
ISBN: 9813275367
Category : Computers
Languages : en
Pages : 736
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Book Description
Recommender systems are very popular nowadays, as both an academic research field and services provided by numerous companies for e-commerce, multimedia and Web content. Collaborative-based methods have been the focus of recommender systems research for more than two decades.The unique feature of the compendium is the technical details of collaborative recommenders. The book chapters include algorithm implementations, elaborate on practical issues faced when deploying these algorithms in large-scale systems, describe various optimizations and decisions made, and list parameters of the algorithms.This must-have title is a useful reference materials for researchers, IT professionals and those keen to incorporate recommendation technologies into their systems and services.
Author: Dietmar Jannach
Publisher: Cambridge University Press
ISBN: 1139492594
Category : Computers
Languages : en
Pages :
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Book Description
In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
Author: Yi-Hsuan Yang
Publisher: CRC Press
ISBN: 143985047X
Category : Computers
Languages : en
Pages : 251
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Book Description
Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with
Author: Francesco Ricci
Publisher: Springer
ISBN: 148997637X
Category : Computers
Languages : en
Pages : 1008
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Book Description
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319296590
Category : Computers
Languages : en
Pages : 518
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Book Description
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
Author: Ambeth Kumar Visvam Devadoss
Publisher: Springer Nature
ISBN: 946463250X
Category : Computers
Languages : en
Pages : 198
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Book Description
This is an open access book. PECTEAM, being held for a period of two days, aims to witness the development of technologies in all technical and management domains. The major event in the conference is paper presentations on the latest advances in Engineering and Management disciplines from National and International academic sectors. Special emphasis is given to update newer technologies by Keynote speakers. PECTEAM is a premier platform for researchers and industry practitioners to share their new and innovative ideas, original research findings and practical development experiences in Engineering and Management through high quality peer reviewed papers.
Author: Mats Nordahl
Publisher:
ISBN: 9789189262041
Category : Computer composition
Languages : en
Pages : 602
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Book Description
