Recommender System For Improving Customer Loyalty
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| Author | : Katarzyna Tarnowska |
| Publisher | : Springer |
| Total Pages | : 133 |
| Release | : 2019-03-19 |
| Genre | : Technology & Engineering |
| ISBN | : 3030134385 |
This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience. The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to “weigh” these actions and determine which ones would have a greater impact.
| Author | : ASHISH KUMAR , ABHISHEK DAS, SHYAMAKRISHNA SIDDHARTH CHAMARTHY, PROF. (DR) PUNIT GOEL |
| Publisher | : DeepMisti Publication |
| Total Pages | : 170 |
| Release | : 2024-10-19 |
| Genre | : Computers |
| ISBN | : 9360440876 |
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, Deep Learning Explained: Theory, Applications, and Future Directions, is conceived to bridge the gap between emerging technological advancements in artificial intelligence and their strategic application across various industries. 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 deep learning technologies, from foundational theories to advanced applications. We delve into the critical aspects that drive successful AI innovations in fields such as healthcare, finance, e-commerce, and autonomous systems. 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, researchers, 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 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 deep learning innovations. 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 deep learning and AI technologies play in shaping the future of industries and 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 deep learning can be harnessed to drive future innovations. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will shape the future of technology. Thank you for joining us on this journey. Authors
| Author | : Katarzyna A. Tarnowska |
| Publisher | : |
| Total Pages | : 124 |
| Release | : 2020 |
| Genre | : COMPUTERS |
| ISBN | : 9783030134396 |
This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience. The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to "learn" from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to "weigh" these actions and determine which ones would have a greater impac t.
| Author | : MURALI MOHANA KRISHNA DANDU RAJAS PARESH KSHIRSAGAR PROF. DR PUNIT GOEL A RENUKA |
| Publisher | : DeepMisti Publication |
| Total Pages | : 239 |
| Release | : 2024-10-12 |
| Genre | : Business & Economics |
| ISBN | : 9360443514 |
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
| Author | : Wang, John |
| Publisher | : IGI Global |
| Total Pages | : 3296 |
| Release | : 2023-01-20 |
| Genre | : Computers |
| ISBN | : 1799892212 |
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.
| Author | : Michael J. Shaw |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 463 |
| Release | : 2006-04-11 |
| Genre | : Computers |
| ISBN | : 0306475480 |
E-Business Management: Integration of Web Technologies with Business Models contains a collection of articles by leading information systems researchers on important topics related to the development of e-business. The goal is to enhance the understanding of the state of the art in e-business, including the most current and forward-looking research. The book emphasizes both business practices and academic research made possible by the recent rapid advances in the applications of e-business technology. The book should help graduate students, researchers, and practitioners understand major e-business developments, how they will transform businesses, and the strategic implications to be drawn.
| Author | : David Heckerman |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 554 |
| Release | : 2014-05-12 |
| Genre | : Computers |
| ISBN | : 1483214516 |
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.
| Author | : Prathmesh Yelne |
| Publisher | : Codegyan |
| Total Pages | : 250 |
| Release | : 2023-08-01 |
| Genre | : Education |
| ISBN | : |
Discover the extraordinary possibilities of machine learning and artificial intelligence in this groundbreaking exploration. From self-driving cars to virtual assistants, this book delves into the fascinating world of algorithms and how they are transforming industries and revolutionizing our lives. Explore the inner workings of neural networks, deep learning models, and predictive analytics, and witness the profound impact they have on decision-making, problem-solving, and data analysis. Whether you're a novice or an expert in the field, prepare to be captivated by the limitless potential of machine learning and AI.
| Author | : Xin-She Yang |
| Publisher | : Springer Nature |
| Total Pages | : 1102 |
| Release | : 2023-07-25 |
| Genre | : Technology & Engineering |
| ISBN | : 9819932432 |
This book gathers selected high-quality research papers presented at the Eighth International Congress on Information and Communication Technology, held at Brunel University, London, on 20–23 February 2023. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The work is presented in four volumes.
| Author | : Kim Normann Andersen |
| Publisher | : Springer |
| Total Pages | : 422 |
| Release | : 2011-08-19 |
| Genre | : Computers |
| ISBN | : 3642229611 |
This book constitutes the refereed proceedings of the Second International Conference on Electronic Government and the Information Systems Perspective, EGOVIS 2011, held in Toulouse, France, in August/September 2011. The 30 revised full papers presented were carefully reviewed and selected from numerous submissions. Among the topics addressed are aspects of security, reliability, privacy and anonymity of e-government systems, knowledge processing, service-oriented computing, and case studies of e-government systems in several countries.
