Recommender Systems

Algorithms and Applications

P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty, P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nandan Mohanty, Sachi Nadan Mohanty, P. Pavan Kumar, S. Vairachilai, Sirisha Potluri, Sachi Nadan Mohanty

• Publisher: CRC Press
• Year: 2021
• Print ISBN: 9780367631857
• eBook ISBN: 9781000387377
• Edition: 1
• Topic: Computer Science
• Subtopic: Consumer Behaviour
• Index: Computer Science

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

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• Machine learning algorithms

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• Community detection algorithms

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• 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

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• A latent-factor technique for model-based filtering systems

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• Collaborative filtering approaches

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• Content-based approaches

Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgements
• Editors
• List of Contributors
• Chapter 1: Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms
• Chapter 2: An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset
• Chapter 3: Why This Recommendation?: Explainable Product Recommendations with Ontological Knowledge Reasoning
• Chapter 4: Model-based Filtering Systems using a Latent-factor Technique
• Chapter 5: Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches
• Chapter 6: Recommendation System for Risk Assessment in Requirements Engineering of Software with Tropos Goal–Risk Model
• Chapter 7: A Comprehensive Overview to the Recommender System: Approaches, Algorithms and Challenges
• Chapter 8: Collaborative Filtering Techniques: Algorithms and Advances
• Index