Big Data Recommender Systems, Volume 1 - Algorithms, Architectures, Big Data, Security and Trust

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the v...

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Bibliographic Details
Main Authors Khan Samee U, Zomaya Albert Y, Khalid Osman
Format eBook
LanguageEnglish
Published Institution of Engineering and Technology (The IET) 2019
Subjects
General References
Software Engineering
Online AccessGet full text
ISBN9811785619
9789811785610

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Table of Contents:
  • Title Page Table of Contents 1. Introduction to Big Data Recommender Systems - Volume 1 2. Theoretical Foundations for Recommender Systems 3. Benchmarking Big Data Recommendation Algorithms Using Hadoop or Apache Spark 4. Efficient and Socio-Aware Recommendation Approaches for Big Data Networked Systems 5. Novel Hybrid Approaches for Big Data Recommendations 6. Deep Generative Models for Recommender Systems 7. Recommendation Algorithms for Unstructured Big Data Such as Text, Audio, Image and Video 8. Deep Segregation of Plastic (DSP): Segregation of Plastic and Nonplastic Using Deep Learning 9. Spatiotemporal Recommendation with Big Geo-Social Networking Data 10. Recommender System for Predicting Malicious Android Applications 11. Security Threats and Their Mitigation in Big Data Recommender Systems 12. User's Privacy in Recommendation Systems Applying Online Social Network Data: A Survey and Taxonomy 13. Private Entity Resolution for Big Data on Apache Spark Using Multiple Phonetic Codes 14. Deep Learning Architecture for Big Data Analytics in Detecting Intrusions and Malicious URL Index