Knowledge Recommendation Systems With Machine Intelligence Algorithms

Author : Jarosław Protasiewicz
Publisher : Springer Nature
Page : 139 pages
File Size : 52,6 Mb
Release : 2023-11-01
Category : Computers
ISBN : 9783031326967

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Knowledge Recommendation Systems with Machine Intelligence Algorithms by Jarosław Protasiewicz Pdf

Knowledge recommendation is an timely subject that is encountered frequently in research and information services. A compelling and urgent need exists for such systems: the modern economy is in dire need of highly-skilled professionals, researchers, and innovators, who create opportunities to gain competitive advantage and assist in the management of financial resources and available goods, as well as conducting fundamental and applied research more effectively. This book takes readers on a journey into the world of knowledge recommendation, and of systems of knowledge recommendation that use machine intelligence algorithms. It illustrates knowledge recommendation using two examples. The first is the recommendation of reviewers and experts who can evaluate manuscripts of academic articles, or of research and development project proposals. The second is innovation support, which involves bringing science and business together by recommending information that pertains to innovations, projects, prospective partners, experts, and conferences meaningfully. The book also describes the selection of the algorithms that transform data into information and then into knowledge, which is then used in the information systems. More specifically, recommendation and information extraction algorithms are used to acquire data, classify publications, identify (disambiguate) their authors, extract keywords, evaluate whether enterprises are innovative, and recommend knowledge. This book comprises original work and is unique in many ways. The systems and algorithms it presents are informed by contemporary solutions described in the literature - including many compelling, novel, and original aspects. The new and promising directions the book presents, as well as the techniques of machine learning applied to knowledge recommendation, are all original.

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 47,8 Mb
Release : 2020-07-08
Category : Computers
ISBN : 9781119711575

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Recommender System with Machine Learning and Artificial Intelligence by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta Pdf

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare 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. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

Author : Francesco Ricci,Lior Rokach,Bracha Shapira,Paul B. Kantor
Publisher : Springer Science & Business Media
Page : 842 pages
File Size : 44,6 Mb
Release : 2010-10-21
Category : Computers
ISBN : 9780387858203

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Recommender Systems Handbook by Francesco Ricci,Lior Rokach,Bracha Shapira,Paul B. Kantor Pdf

The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

Author : Jie Lu,Qian Zhang,Guang-quan Zhang
Publisher : World Scientific
Page : 362 pages
File Size : 50,8 Mb
Release : 2020-08-04
Category : Computers
ISBN : 9789811224645

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Recommender Systems: Advanced Developments by Jie Lu,Qian Zhang,Guang-quan Zhang Pdf

Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Author : Oliver Theobald
Publisher : Packt Publishing Ltd
Page : 131 pages
File Size : 47,6 Mb
Release : 2024
Category : Electronic books
ISBN : 9781835882078

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MACHINE LEARNING by Oliver Theobald Pdf

Author : Akshay Kulkarni,Adarsha Shivananda,Anoosh Kulkarni,V Adithya Krishnan
Publisher : Apress
Page : 0 pages
File Size : 54,9 Mb
Release : 2022-12-08
Category : Computers
ISBN : 1484289536

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Applied Recommender Systems with Python by Akshay Kulkarni,Adarsha Shivananda,Anoosh Kulkarni,V Adithya Krishnan Pdf

This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 49,8 Mb
Release : 2024-06-03
Category : Electronic
ISBN : 9789814471503

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Personalization Techniques and Recommender Systems by Anonim Pdf

Author : Rounak Banik
Publisher : Packt Publishing Ltd
Page : 141 pages
File Size : 42,8 Mb
Release : 2018-07-31
Category : Computers
ISBN : 9781788992534

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Hands-On Recommendation Systems with Python by Rounak Banik Pdf

With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta
Publisher : John Wiley & Sons
Page : 448 pages
File Size : 43,6 Mb
Release : 2020-06-09
Category : Computers
ISBN : 9781119711599

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Recommender System with Machine Learning and Artificial Intelligence by Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Sarika Jain,Ahmed A. Elngar,Priya Gupta Pdf

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare 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. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

Author : Charu C. Aggarwal
Publisher : Springer
Page : 498 pages
File Size : 44,8 Mb
Release : 2016-03-28
Category : Computers
ISBN : 9783319296593

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Recommender Systems by Charu C. Aggarwal Pdf

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 : Frank Hopfgartner
Publisher : Springer
Page : 372 pages
File Size : 44,7 Mb
Release : 2015-01-14
Category : Computers
ISBN : 9783319141787

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Smart Information Systems by Frank Hopfgartner Pdf

This text presents an overview of smart information systems for both the private and public sector, highlighting the research questions that can be studied by applying computational intelligence. The book demonstrates how to transform raw data into effective smart information services, covering the challenges and potential of this approach. Each chapter describes the algorithms, tools, measures and evaluations used to answer important questions. This is then further illustrated by a diverse selection of case studies reflecting genuine problems faced by SMEs, multinational manufacturers, service companies, and the public sector. Features: provides a state-of-the-art introduction to the field, integrating contributions from both academia and industry; reviews novel information aggregation services; discusses personalization and recommendation systems; examines sensor-based knowledge acquisition services, describing how the analysis of sensor data can be used to provide a clear picture of our world.

Author : Aristomenis S. Lampropoulos,George A. Tsihrintzis
Publisher : Springer
Page : 125 pages
File Size : 43,6 Mb
Release : 2015-06-13
Category : Technology & Engineering
ISBN : 9783319191355

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Machine Learning Paradigms by Aristomenis S. Lampropoulos,George A. Tsihrintzis Pdf

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.

Author : P. Pavan Kumar,S. Vairachilai,Sirisha Potluri,Sachi Nadan Mohanty
Publisher : CRC Press
Page : 182 pages
File Size : 44,9 Mb
Release : 2021-06-01
Category : Computers
ISBN : 9781000387377

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Recommender Systems by P. Pavan Kumar,S. Vairachilai,Sirisha Potluri,Sachi Nadan Mohanty Pdf

Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

Author : Elsa Nègre
Publisher : John Wiley & Sons
Page : 98 pages
File Size : 50,5 Mb
Release : 2015-10-12
Category : Computers
ISBN : 9781848217546

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Information and Recommender Systems by Elsa Nègre Pdf

Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation. In a context of steady increase in the mass of information it is difficult to know what information to look for and where to find them. Computer techniques exist to facilitate this research and allow relevant information extraction. Recommendation systems introduced the notions inherent to the recommendation, based, inter alia, information search, filtering, machine learning, collaborative approaches. It also deals with the assessment of such systems and has various applications.

Author : Frank Kane
Publisher : Unknown
Page : 512 pages
File Size : 49,7 Mb
Release : 2018-08-11
Category : Computers
ISBN : 1718120125

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Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Mach by Frank Kane Pdf

Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies.You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you'll have access to all of the source code associated with it as well.We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover: -Building a recommendation engine-Evaluating recommender systems-Content-based filtering using item attributes-Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF-Model-based methods including matrix factorization and SVD-Applying deep learning, AI, and artificial neural networks to recommendations-Session-based recommendations with recursive neural networks-Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines-Real-world challenges and solutions with recommender systems-Case studies from YouTube and Netflix-Building hybrid, ensemble recommendersThis comprehensive book takes you all the way from the early days of collaborative filtering, to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user.The coding exercises for this book use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this book successfully. We also include a short introduction to deep learning, Tensorfow, and Keras if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms.Dive in, and learn about one of the most interesting and lucrative applications of machine learning and deep learning there is!