Description
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.
Table of Contents
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
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- 電子書籍
- 週刊プロレス 2019年 10/23号…
