Reviews In Recommender Systems 2022

Author : Dominik Kowald
Publisher : Frontiers Media SA
ISBN 13 : 2832547664
Total Pages : 133 pages
Book Rating : 4.63/5 ( download)

DOWNLOAD NOW!

Download or read book Reviews in Recommender Systems: 2022 written by Dominik Kowald and published by Frontiers Media SA. This book was released on 2024-04-10 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frontiers in Big Data is delighted to present the ‘Reviews in Recommender Systems’ series of article collections. Reviews in Recommender Systems will publish high-quality scholarly review papers on key topics in recommender systems and their applications in our everyday lives, in search engines, online retail, news, entertainment, travel, social networks, and much more. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. We anticipate the research presented will promote discussion in the Big Data community that will translate to best practice applications in further research, industry, real-world implementations, public health, and policy settings.

Author : Kim Falk
Publisher : Simon and Schuster
ISBN 13 : 1638353980
Total Pages : 743 pages
Book Rating : 4.80/5 ( download)

DOWNLOAD NOW!

Download or read book Practical Recommender Systems written by Kim Falk and published by Simon and Schuster. This book was released on 2019-01-18 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. About the Book Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. What's inside How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the Reader Readers need intermediate programming and database skills. About the Author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Table of Contents PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS What is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate them Non-personalized recommendations The user (and content) who came in from the cold PART 2 - RECOMMENDER ALGORITHMS Finding similarities among users and among content Collaborative filtering in the neighborhood Evaluating and testing your recommender Content-based filtering Finding hidden genres with matrix factorization Taking the best of all algorithms: implementing hybrid recommenders Ranking and learning to rank Future of recommender systems

Author : Humberto Jesús Corona Pampín
Publisher : Springer Nature
ISBN 13 : 3031221923
Total Pages : 125 pages
Book Rating : 4.27/5 ( download)

DOWNLOAD NOW!

Download or read book Recommender Systems in Fashion and Retail written by Humberto Jesús Corona Pampín and published by Springer Nature. This book was released on 2023-03-01 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the fourth workshop on recommender systems in fashion and retail (2022), and it aims to present a state-of-the-art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail, and fashion by presenting readers with chapters covering contributions from academic as well as industrial researchers active within this emerging new field. Recommender systems are often used to solve different complex problems in this scenario, such as product recommendations, size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 518 pages
Book Rating : 4.93/5 ( download)

DOWNLOAD NOW!

Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1008 pages
Book Rating : 4.76/5 ( download)

DOWNLOAD NOW!

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Yasamin Heshmat Dehkordi
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.14/5 ( download)

DOWNLOAD NOW!

Download or read book Incorporating User Reviews as Implicit Feedback for Improving Recommender Systems written by Yasamin Heshmat Dehkordi and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommendation systems have become extremely common in recent years due tothe ubiquity of information across various applications. Online entertainment (e.g.,Netflix), E-commerce (e.g., Amazon, Ebay) and publishing services such as GoogleNews are all examples of services which use recommender systems. Recommendation systems are rapidly evolving in these years, but these methods have fallen short in coping with several emerging trends such as likes or votes on reviews. In this work we have proposed a new method based on collaborative filtering by considering other users' feedback on each review. To validate our approach we have used Yelp data set with more than 335,000 product and service category ratings and 70,817 real users. We present our results using comparative analysis with other well-known recommendation systems for particular categories of users and items.

Author : Osvaldo Gervasi
Publisher : Springer Nature
ISBN 13 : 3031371054
Total Pages : 781 pages
Book Rating : 4.59/5 ( download)

DOWNLOAD NOW!

Download or read book Computational Science and Its Applications – ICCSA 2023 Workshops written by Osvaldo Gervasi and published by Springer Nature. This book was released on 2023-06-28 with total page 781 pages. Available in PDF, EPUB and Kindle. Book excerpt: This nine-volume set LNCS 14104 – 14112 constitutes the refereed workshop proceedings of the 23rd International Conference on Computational Science and Its Applications, ICCSA 2023, held at Athens, Greece, during July 3–6, 2023. The 350 full papers and 29 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 876 submissions. These nine-volumes includes the proceedings of the following workshops: Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2023); Advanced Processes of Mathematics and Computing Models in Complex Computational Systems (ACMC 2023); Artificial Intelligence supported Medical data examination (AIM 2023); Advanced and Innovative web Apps (AIWA 2023); Assessing Urban Sustainability (ASUS 2023); Advanced Data Science Techniques with applications in Industry and Environmental Sustainability (ATELIERS 2023); Advances in Web Based Learning (AWBL 2023); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2023); Bio and Neuro inspired Computing and Applications (BIONCA 2023); Choices and Actions for Human Scale Cities: Decision Support Systems (CAHSC-DSS 2023); and Computational and Applied Mathematics (CAM 2023).

Author : Monideepa Roy
Publisher : CRC Press
ISBN 13 : 100088628X
Total Pages : 261 pages
Book Rating : 4.83/5 ( download)

DOWNLOAD NOW!

Download or read book Recommender Systems written by Monideepa Roy and published by CRC Press. This book was released on 2023-06-19 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.

Author : Reza Ravanmehr
Publisher : Springer Nature
ISBN 13 : 3031425596
Total Pages : 314 pages
Book Rating : 4.92/5 ( download)

DOWNLOAD NOW!

Download or read book Session-Based Recommender Systems Using Deep Learning written by Reza Ravanmehr and published by Springer Nature. This book was released on 2024-01-21 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the widespread use of deep neural networks and their various techniques in session-based recommender systems (SBRS). It presents the success of using deep learning techniques in many SBRS applications from different perspectives. For this purpose, the concepts and fundamentals of SBRS are fully elaborated, and different deep learning techniques focusing on the development of SBRS are studied. The book is well-modularized, and each chapter can be read in a stand-alone manner based on individual interests and needs. In the first chapter of the book, definitions and concepts related to SBRS are reviewed, and a taxonomy of different SBRS approaches is presented, where the characteristics and applications of each class are discussed separately. The second chapter starts with the basic concepts of deep learning and the characteristics of each model. Then, each deep learning model, along with its architecture and mathematical foundations, is introduced. Next, chapter 3 analyses different approaches of deep discriminative models in session-based recommender systems. In the fourth chapter, session-based recommender systems that benefit from deep generative neural networks are discussed. Subsequently, chapter 5 discusses session-based recommender systems using advanced/hybrid deep learning models. Eventually, chapter 6 reviews different learning-to-rank methods focusing on information retrieval and recommender system domains. Finally, the results of the investigations and findings from the research review conducted throughout the book are presented in a conclusive summary. This book aims at researchers who intend to use deep learning models to solve the challenges related to SBRS. The target audience includes researchers entering the field, graduate students specializing in recommender systems, web data mining, information retrieval, or machine/deep learning, and advanced industry developers working on recommender systems.

Author : Yiliu Tu
Publisher : Springer Nature
ISBN 13 : 3031322991
Total Pages : 388 pages
Book Rating : 4.90/5 ( download)

DOWNLOAD NOW!

Download or read book E-Business. Digital Empowerment for an Intelligent Future written by Yiliu Tu and published by Springer Nature. This book was released on 2023-05-08 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNBIP 480 and 481 constitutes the refereed proceedings of the 22nd Wuhan International Conference, WHICEB 2023, held in Wuhan, China, in May 2023. The 61 full papers presented in these proceedings were carefully reviewed and selected from 350 submissions. They focus on innovative research findings, solutions, and approaches to make the Internet a productive and efficient vehicle for global commerce. This year’s topic is “Digital Empowerment for an Intelligent Future“.