Recommender Systems Algorithms and Applications

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.

Preface Acknowledgements Editors List of Contributors Chapter 1 Collaborative Filtering-based Robust Recommender System using Machine Learning Algorithms UTKARSH PRAVIND, PALAK PORWAL, ABHAYA KUMAR SAHOO AND CHITTARANJAN PRADHAN Chapter 2 An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset B.S.A.S. RAJITA, MRINALINI SHUKLA, DEEPA KUMARI AND SUBHRAKANTA PANDA Chapter 3 Why This Recommendation: Explainable Product Recommendations with Ontological Knowledge Reasoning VIDYA KAMMA, D. TEJA SANTOSH AND SRIDEVI GUTTA Chapter 4 Model-based Filtering Systems using a Latent-factor Technique ALEENA MISHRA, MAHENDRA KUMAR GOURISARIA, PALAK GUPTA, SUDHANSU SHEKHAR PATRA AND LALBIHARI BARIK Chapter 5 Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches R. S. M. LAKSHMI PATIBANDLA, V. LAKSHMAN NARAYANA, AREPALLI PEDA GOPI AND B. TARAKESWARA RAO Chapter 6 Recommendation System for Risk Assessment in Requirements Engineering of Software with Tropos Goal–Risk Model G. RAMESH, P. DILEEP KUMAR REDDY, J. SOMASEKAR AND S. ANUSHA Chapter 7 A Comprehensive Overview to the Recommender System: Approaches, Algorithms and Challenges R. BHUVANYA AND M. KAVITHA Chapter 8 Collaborative Filtering Techniques: Algorithms and Advances PALLAVI MISHRA AND SACHI NANDAN MOHANTY Index

Dr. P. Pavan Kumar received a Ph.D. degree from JNTU, Anantapur, India. He is an Assistant Professor in the Department of Computer Science and Engineering at ICFAI Foundation for Higher Education (IFHE), Hyderabad. His research interests include real-time systems, multi-core systems, high-performance systems, computer vision. Dr. S. Vairachilai earned a Ph.D. degree in Information Technology from Anna University, India. She is an Assistant Professor in the Department of CSE at ICFAI Foundation for Higher Education (IFHE), Hyderabad, Telangana. Prior to this she served in teaching roles an Kalasalingam University and N.P.R College of Engineering and Technology, Tamilnadu, India. Her research interests include Machine Learning, Recommender System and Social Network Analysis. Sirisha Potluri is an Assistant Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad. She is pursuing a Ph.D. degree in the area of cloud computing. Her research areas include distributed computing, cloud computing, fog computing, recommender systems and IoT. Dr. Sachi Nandan Mohanty received a Ph.D. degree from IIT Kharagpur, India. He is an Associate Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. Prof. Mohanty’s research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.