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Recommender Systems

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academic resource recommendation
adversarial robustness
AI
Algorithms
behavioral analytics
Category=UMB
Category=UMZ
Category=UN
Category=UYQ
CF
Cf Method
Cold Start Problem
computational intelligence
Consumar Behaviour
Content Based Recommender Systems
Data Mining
Data Processing Module
Data Set
Decision Support Systems
Deep Neural Networks
Embedding Vectors
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Facial Landmarks
Gaussian Mixture Model
Hybrid Recommender System
IoT Device
Machine Learning
Machine Learning/AI
Machine LearningAI
MIT Open Courseware
PSO Technique
Rec
Recommender System
Road Segments
Road Traffic Data
robust algorithm evaluation techniques
Roc Curve
RSU
Smart Parking
Smart Phone
Support Vector Machine
swarm intelligence methods
TPB Factor
trust modeling
User Response
Web App

Product details

  • ISBN 9781032333212
  • Weight: 670g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Jun 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About
Monideepa Roy, Pushpendu Kar, Sujoy Datta