Recommendation Systems In Software Engineering
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Recommendation Systems In Software Engineering
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Author : Martin P. Robillard
language : en
Publisher: Springer Science & Business
Release Date : 2014-04-30
Recommendation Systems In Software Engineering written by Martin P. Robillard and has been published by Springer Science & Business this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-30 with Computers categories.
With the growth of public and private data stores and the emergence of off-the-shelf data-mining technology, recommendation systems have emerged that specifically address the unique challenges of navigating and interpreting software engineering data. This book collects, structures and formalizes knowledge on recommendation systems in software engineering. It adopts a pragmatic approach with an explicit focus on system design, implementation, and evaluation. The book is divided into three parts: “Part I – Techniques” introduces basics for building recommenders in software engineering, including techniques for collecting and processing software engineering data, but also for presenting recommendations to users as part of their workflow. “Part II – Evaluation” summarizes methods and experimental designs for evaluating recommendations in software engineering. “Part III – Applications” describes needs, issues and solution concepts involved in entire recommendation systems for specific software engineering tasks, focusing on the engineering insights required to make effective recommendations. The book is complemented by the webpage rsse.org/book, which includes free supplemental materials for readers of this book and anyone interested in recommendation systems in software engineering, including lecture slides, data sets, source code, and an overview of people, groups, papers and tools with regard to recommendation systems in software engineering. The book is particularly well-suited for graduate students and researchers building new recommendation systems for software engineering applications or in other high-tech fields. It may also serve as the basis for graduate courses on recommendation systems, applied data mining or software engineering. Software engineering practitioners developing recommendation systems or similar applications with predictive functionality will also benefit from the broad spectrum of topics covered.
Recommender Systems
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Author : P. Pavan Kumar
language : en
Publisher:
Release Date : 2021
Recommender Systems written by P. Pavan Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Recommender Systems
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Author : Monideepa Roy
language : en
Publisher: CRC Press
Release Date : 2023-06-19
Recommender Systems written by Monideepa Roy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-19 with Computers categories.
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.
4th International Workshop On Recommendation Systems For Software Engineering Proceedings June 3 2014 Hyderabad India
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Author : Reid Holmes
language : en
Publisher:
Release Date : 2014
4th International Workshop On Recommendation Systems For Software Engineering Proceedings June 3 2014 Hyderabad India written by Reid Holmes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computer software categories.
Building Recommendation Systems In Python And Jax
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Author : Bryan Bischof Ph.D
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-12-04
Building Recommendation Systems In Python And Jax written by Bryan Bischof Ph.D and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-04 with Computers categories.
Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. You'll learn: The data essential for building a RecSys How to frame your data and business as a RecSys problem Ways to evaluate models appropriate for your system Methods to implement, train, test, and deploy the model you choose Metrics you need to track to ensure your system is working as planned How to improve your system as you learn more about your users, products, and business case
Hands On Recommendation Systems With Python
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Author : Rounak Banik
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Hands On Recommendation Systems With Python written by Rounak Banik and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-31 with Computers categories.
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.
Proceedings Of The 2008 International Workshop On Recommendation Systems For Software Engineering
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Author :
language : en
Publisher:
Release Date : 2008
Proceedings Of The 2008 International Workshop On Recommendation Systems For Software Engineering written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computer science categories.
Recommender Systems
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Author : P. Pavan Kumar
language : en
Publisher: CRC Press
Release Date : 2021-06-01
Recommender Systems written by P. Pavan Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-01 with Computers categories.
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.
Industrial Recommender System
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Author : Lantao Hu
language : en
Publisher: Springer
Release Date : 2024-07-12
Industrial Recommender System written by Lantao Hu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-12 with Computers categories.
Recommender systems, as a highly popular AI technology in recent years, have been widely applied across various industries. They have transformed the way we interact with technology, influencing our choices and shaping our experiences. This book provides a comprehensive introduction to industrial recommender systems, starting with the overview of the technical framework, gradually delving into each core module such as content understanding, user profiling, recall, ranking, re-ranking and so on, and introducing the key technologies and practices in enterprises. The book also addresses common challenges in recommendation cold start, recommendation bias and debiasing. Additionally, it introduces advanced technologies in the field, such as reinforcement learning, causal inference. Professionals working in the fields of recommender systems, computational advertising, and search will find this book valuable. It is also suitable for undergraduate, graduate, and doctoral students majoring in artificial intelligence, computer science, software engineering, and related disciplines. Furthermore, it caters to readers with an interest in recommender systems, providing them with an understanding of the foundational framework, insights into core technologies, and advancements in industrial recommender systems. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Recommendation System Using Python
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Author : Dr. Vikas Thada
language : en
Publisher: BookRix
Release Date : 2020-05-26
Recommendation System Using Python written by Dr. Vikas Thada and has been published by BookRix this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-26 with Computers categories.
In the very recent years, development of recommendation system has been a more heated problem due to a higher level of data consumption and the advancement of machine learning techniques The book presents an improved algorithm based on machine learning on hybrid approach using collaborative filtering, content based filtering and popularity based filtering using python