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Full Description
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 volumes of data, which has introduced many new challenges.
Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.
Contents
Chapter 1: Introduction to big data recommender systems - volume 1
Chapter 2: Theoretical foundations for recommender systems
Chapter 3: Benchmarking big data recommendation algorithms using Hadoop orApache Spark
Chapter 4: Efficient and socio-aware recommendation approaches for bigdata networked systems
Chapter 5: Novel hybrid approaches for big data recommendations
Chapter 6: Deep generative models for recommender systems
Chapter 7: Recommendation algorithms for unstructured big data such as text, audio, image and video
Chapter 8: Deep segregation of plastic (DSP): segregation of plastic and nonplastic using deep learning
Chapter 9: Spatiotemporal recommendation with big geo-social networking data
Chapter 10: Recommender system for predicting malicious Android applications
Chapter 11: Security threats and their mitigation in big data recommender systems
Chapter 12: User's privacy in recommendation systems applying online social network data: a survey and taxonomy
Chapter 13: Private entity resolution for big data on Apache Spark using multiple phonetic codes
Chapter 14: Deep learning architecture for big data analytics in detecting intrusions and malicious URL
