Explainable Recommendation
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Explainable Recommendation
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Author : Yongfeng Zhang
language : en
Publisher:
Release Date : 2020-03-10
Explainable Recommendation written by Yongfeng Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-10 with Computers categories.
In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Recommender Systems
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Author : Dongsheng Li
language : en
Publisher: Springer Nature
Release Date : 2024-03-25
Recommender Systems written by Dongsheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-25 with Computers categories.
This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
Explainable Interpretable And Transparent Ai Systems
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Author : B. K. Tripathy
language : en
Publisher: CRC Press
Release Date : 2024-08-23
Explainable Interpretable And Transparent Ai Systems written by B. K. Tripathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-23 with Technology & Engineering categories.
Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains. Features: Presents a clear focus on the application of explainable AI systems while tackling important issues of “interpretability” and “transparency”. Reviews adept handling with respect to existing software and evaluation issues of interpretability. Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression. Focuses on interpreting black box models like feature importance and accumulated local effects. Discusses capabilities of explainability and interpretability. This book is aimed at graduate students and professionals in computer engineering and networking communications.
Multimodal Learning Toward Recommendation
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Author : Fan Liu
language : en
Publisher: Springer Nature
Release Date : 2025-01-17
Multimodal Learning Toward Recommendation written by Fan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-17 with Mathematics categories.
This book presents an in-depth exploration of multimodal learning toward recommendation, along with a comprehensive survey of the most important research topics and state-of-the-art methods in this area. First, it presents a semantic-guided feature distillation method which employs a teacher-student framework to robustly extract effective recommendation-oriented features from generic multimodal features. Next, it introduces a novel multimodal attentive metric learning method to model user diverse preferences for various items. Then it proposes a disentangled multimodal representation learning recommendation model, which can capture users’ fine-grained attention to different modalities on each factor in user preference modeling. Furthermore, a meta-learning-based multimodal fusion framework is developed to model the various relationships among multimodal information. Building on the success of disentangled representation learning, it further proposes an attribute-driven disentangled representation learning method, which uses attributes to guide the disentanglement process in order to improve the interpretability and controllability of conventional recommendation methods. Finally, the book concludes with future research directions in multimodal learning toward recommendation. The book is suitable for graduate students and researchers who are interested in multimodal learning and recommender systems. The multimodal learning methods presented are also applicable to other retrieval or sorting related research areas, like image retrieval, moment localization, and visual question answering.
Explainable Ai Within The Digital Transformation And Cyber Physical Systems
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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer Nature
Release Date : 2021-10-30
Explainable Ai Within The Digital Transformation And Cyber Physical Systems written by Moamar Sayed-Mouchaweh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-30 with Technology & Engineering categories.
This book presents Explainable Artificial Intelligence (XAI), which aims at producing explainable models that enable human users to understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowledge (managers, engineers, technicians, etc.) in different application domains. Secondly, that it is important to develop an evaluation framework and standards in order to measure the effectiveness of the provided explanations at the human and the technical levels. This book gathers research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefits and requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions. Includes recent developments of the use of Explainable Artificial Intelligence (XAI) in order to address the challenges of digital transition and cyber-physical systems; Provides a textual scientific description of the use of XAI in order to address the challenges of digital transition and cyber-physical systems; Presents examples and case studies in order to increase transparency and understanding of the methodological concepts.
Explainable Artificial Intelligence Based On Neuro Fuzzy Modeling With Applications In Finance
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Author : Tom Rutkowski
language : en
Publisher: Springer Nature
Release Date : 2021-06-07
Explainable Artificial Intelligence Based On Neuro Fuzzy Modeling With Applications In Finance written by Tom Rutkowski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-07 with Technology & Engineering categories.
The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
Explainable Ai For Education Recent Trends And Challenges
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Author : Tanu Singh
language : en
Publisher: Springer Nature
Release Date : 2024-11-27
Explainable Ai For Education Recent Trends And Challenges written by Tanu Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-27 with Computers categories.
“Explainable AI for Education: Recent Trends and Challenges” is a comprehensive exploration of the intersection between artificial intelligence (AI) and education. In this book, we delve into the critical need for transparency and interpretability in AI systems deployed within educational contexts. Key Themes Understanding AI in Education: We provide a concise overview of AI techniques commonly used in educational settings, including recommendation systems, personalized learning, and assessment tools. Readers will gain insights into the potential benefits and risks associated with AI adoption in education. The Black-Box Problem: AI models often operate as “black boxes,” making it challenging to understand their decision-making processes. We discuss the implications of this opacity and emphasize the importance of explainability. Explainable AI (XAI) Techniques: From rule-based approaches to neural network interpretability, we explore various methods for making AI models more transparent. Examples and case studies illustrate how XAI can enhance educational outcomes. Ethical Considerations: As AI becomes more integrated into education, ethical dilemmas arise. We address issues related to bias, fairness, and accountability, emphasizing responsible AI practices. Future Directions: Our book looks ahead, considering the evolving landscape of AI and its impact on education. We propose research directions and practical steps to promote XAI adoption in educational institutions.
Xxai Beyond Explainable Ai
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Author : Andreas Holzinger
language : en
Publisher: Springer Nature
Release Date : 2022-04-16
Xxai Beyond Explainable Ai written by Andreas Holzinger and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-16 with Computers categories.
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
Distributed Ambient And Pervasive Interactions
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Author : Norbert A. Streitz
language : en
Publisher: Springer Nature
Release Date : 2024-05-31
Distributed Ambient And Pervasive Interactions written by Norbert A. Streitz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
This book constitutes the refereed proceedings of the 12th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2024, held as part of the 26th International Conference on Human-Computer Interaction, HCI International 2024 (HCII 2024), was held as a hybrid event in Washington DC, USA, during June/July 2024. The total of 1271 papers and 309 posters included in the HCII 2023 proceedings was carefully reviewed and selected from 5108 submissions. The DAPI conference addressed approaches and objectives of information, interaction, and user experience design for DAPI Environments as well as their enabling technologies, methods, and platforms, and relevant application areas. The DAPI 2024 conference covered topics addressing basic research questions and technology issues in the areas of new modalities, immersive environments, smart devices, and much more.