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Recommender Systems

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academic recommender system research
Adjacency Matrix
advanced data mining
Baseline Predictors
Category=KJS
Category=UMB
Category=UMZ
Category=UYQ
Cc
Cd
Cd Algorithm
CF System
Cold Start Problem
Collaborating Filtering Methods
Collaborative Filtering
Collaborative Filtering Technique
collaborative filtering techniques
commercial applications
community detection algorithms
content-based approaches
E-business
Edge Betweenness
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
explainable AI systems
Filtering algorithms
Huffman Code
imbalanced dataset solutions
Item Bias
Latent Factor Models
latent factor technique
LDA
Machine Learning algorithms
Matrix Factorization
Matrix Factorization Models
model-based filtering systems
Neighbourhood Model
Non-negative Matrix Factorization
ontological knowledge reasoning
personalized information retrieval
PRA
product recommendations
Rating Matrix
recommender systems
RF
risk assessment
social networking
software risk modeling
Stochastic Gradient Descent
SWRL Rule
Tropos Goal-Risk Model
user preference prediction

Product details

  • ISBN 9780367631857
  • Weight: 485g
  • Dimensions: 156 x 234mm
  • Publication Date: 04 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

About

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.