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).

Download or read book Recommender Systems in Fashion and Retail written by Nima Dokoohaki and published by Springer Nature. This book was released on 2021-03-23 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), 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, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Download or read book Recommender Systems in Fashion and Retail written by Nima Dokoohaki and published by Springer. This book was released on 2022-03-24 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the second workshop on recommender systems in fashion and retail (2020), 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, or size and fit recommendations, and social media-influenced recommendations (outfits worn by influencers).

Download or read book Fashion Recommender Systems written by Nima Dokoohaki and published by Springer Nature. This book was released on 2020-11-04 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes the proceedings of the first workshop on Recommender Systems in Fashion 2019. It presents a state of the art view of the advancements within the field of recommendation systems with focused application to e-commerce, retail and fashion. The volume covers 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 social fashion-based recommendations (outfits inspired by influencers), product recommendations, or size and fit recommendations. The impact of social networks and the influence that fashion influencers have on the choices people make for shopping is undeniable. For instance, many people use Instagram to learn about fashion trends from top influencers, which helps them to buy similar or even exact outfits from the tagged brands in the post. When traced, customers’ social behavior can be a very useful guide for online shopping websites, providing insights on the styles the customers are really interested in, and hence aiding the online shops in offering better recommendations and facilitating customers quest for outfits. Another well known difficulty with recommendation of similar items is the large quantities of clothing items which can be considered similar, but belong to different brands. Relying only on implicit customer behavioral data will not be sufficient in the coming future to distinguish between for recommendation that will lead to an item being purchased and kept, vs. a recommendation that might result in either the customer not following it, or eventually return the item. Finding the right size and fit for clothes is one of the major factors not only impacting customers purchase decision, but also their satisfaction from e-commerce fashion platforms. Moreover, fashion articles have important sizing variations. Finally, customer preferences towards perceived article size and fit for their body remain highly personal and subjective which influences the definition of the right size for each customer. The combination of the above factors leaves the customers alone to face a highly challenging problem of determining the right size and fit during their purchase journey, which in turn has resulted in having more than one third of apparel returns to be caused by not ordering the right article size. This challenge presents a huge opportunity for research in intelligent size and fit recommendation systems and machine learning solutions with direct impact on both customer satisfaction and business profitability.

Download or read book Recommender Systems written by Dietmar Jannach and published by Cambridge University Press. This book was released on 2010-09-30 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

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.

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer Nature. This book was released on 2022-04-21 with total page 1053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.

Download or read book Information Systems for the Fashion and Apparel Industry written by Tsan-Ming Jason Choi and published by Woodhead Publishing. This book was released on 2016-04-13 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information Systems for the Fashion and Apparel Industry brings together trends and developments in fashion information systems, industrial case-studies, and insights from an international team of authors. The fashion and apparel industry is fast-growing and highly influential. Computerized information systems are essential to support fashion business operations and recent developments in social media, mobile commerce models, radio frequency identification (RFID) technologies, and ERP systems are all driving innovative business measures in the industry. After an introductory chapter outlining key decision points and information requirements in fast fashion supply chains, Part One focuses on the principles of fashion information systems, with chapters covering how decision making in the apparel supply chains can be improved through the use of fuzzy logic, RFID technologies, evolutionary optimization techniques, and artificial neural networks. Part Two then reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns. - Provides systematic and comprehensive coverage of information systems for the fashion and apparel industry - Combines recent developments and industrial best-practices in apparel supply chain management in order to meet the needs of the fashion and apparel industry professionals and academics - Features input from a team of highly knowledgeable authors with a range of professional and academic experience, overseen by an editor who is a leading expert in the field - Reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns

Download or read book Reinventing Fashion Retailing written by Eirini Bazaki and published by Springer Nature. This book was released on 2023-01-01 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of digital trends, innovations, and strategies in fashion retailing. As consumers adopt new technologies and ways of shopping, fashion brands are constantly looking for ways to innovate and achieve digital transformation. Combining theory with practice, the authors take a deep dive into the impact of digital technologies on fashion brands communication and social media strategies; on consumer behaviour and customer participation strategies; and on entrepreneurship and e-tailing strategies. The book covers topics such as Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), fashion recommender systems, virtual fitting rooms, customer models, gamification, online shopping, mobile-shopping, videogames, digital media and virtual worlds. The book also explores the concepts of cocreation, storytelling and interactivity in real-life crowdfunding campaigns and in the digital world. Bringing a cutting-edge insight into the state of the fashion business, this book will help scholars and practitioners in fashion retailing, discover how to digitalise and gamify products, services, experiences and open new enterprising avenues through innovative strategies, leadership and management.

Download or read book AI DRIVEN RECOMMENDER SYSTEMS IN E COMMERCE written by MURALI MOHANA KRISHNA DANDU RAJAS PARESH KSHIRSAGAR PROF. DR PUNIT GOEL A RENUKA and published by DeepMisti Publication. This book was released on 2024-10-12 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, AI-Driven Recommender Systems in E-commerce, is conceived with the purpose of bridging the gap between emerging technological advancements in artificial intelligence and their strategic application in e-commerce management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of AI technologies, particularly recommender systems, and their integration into e-commerce practices. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in online retail environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world e-commerce scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and AI adoption to the strategic management of innovation in online retail. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that AI-driven recommender systems and e-commerce management play in shaping the future of digital businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how AI technologies and e-commerce management can be harnessed together to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will define the future of e-commerce. Thank you for joining us on this journey. Authors

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Download or read book Generative Adversarial Networks for Image to Image Translation written by Arun Solanki and published by Academic Press. This book was released on 2021-06-22 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. - Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN - Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks - Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications

Download or read book Pro Machine Learning Algorithms written by V Kishore Ayyadevara and published by Apress. This book was released on 2018-06-30 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Download or read book Building Web Reputation Systems written by Randy Farmer and published by "O'Reilly Media, Inc.". This book was released on 2010-03-04 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: What do Amazon's product reviews, eBay's feedback score system, Slashdot's Karma System, and Xbox Live's Achievements have in common? They're all examples of successful reputation systems that enable consumer websites to manage and present user contributions most effectively. This book shows you how to design and develop reputation systems for your own sites or web applications, written by experts who have designed web communities for Yahoo! and other prominent sites. Building Web Reputation Systems helps you ask the hard questions about these underlying mechanisms, and why they're critical for any organization that draws from or depends on user-generated content. It's a must-have for system architects, product managers, community support staff, and UI designers. Scale your reputation system to handle an overwhelming inflow of user contributions Determine the quality of contributions, and learn why some are more useful than others Become familiar with different models that encourage first-class contributions Discover tricks of moderation and how to stamp out the worst contributions quickly and efficiently Engage contributors and reward them in a way that gets them to return Examine a case study based on actual reputation deployments at industry-leading social sites, including Yahoo!, Flickr, and eBay

Download or read book Machine Learning Applications Using Python written by Puneet Mathur and published by Apress. This book was released on 2018-12-12 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will LearnDiscover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

Download or read book Retailing in the 21st Century written by Manfred Krafft and published by Springer Science & Business Media. This book was released on 2009-12-17 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: With crisp and insightful contributions from 47 of the world’s leading experts in various facets of retailing, Retailing in the 21st Century offers in one book a compendium of state-of-the-art, cutting-edge knowledge to guide successful retailing in the new millennium. In our competitive world, retailing is an exciting, complex and critical sector of business in most developed as well as emerging economies. Today, the retailing industry is being buffeted by a number of forces simultaneously, for example the growth of online retailing and the advent of ‘radio frequency identification’ (RFID) technology. Making sense of it all is not easy but of vital importance to retailing practitioners, analysts and policymakers.