The Institution of Engineering and Technology, 2019. — 367 p. — (IET Professional Applications of Computing Series 35). — ISBN 981-1-78561-975-5.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 23 chapters. Volume 1 is aimed to cover the recent advances, issues, novel solutions, and theoretical research on big data recommender systems. The book encompasses original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques, and tools for recommender systems. A specific focus is devoted to emerging trends and the industry needs associated with utilizing recommender systems. Some of the topics covered in the Volume 1 include benchmarking of recommendation algorithms using Map Reduce, social recommendations, hybrid approaches (HAs), deep learning-based techniques, unstructured big data recommendations, machine learning (ML)-based models, and geo-social recommendations. A special section is included to cover the security and privacy concerns, cyberattacks on recommender systems, and their defensive measures. Contents Introduction to big data recommender systems—volume 1 Theoretical foundations for recommender systems Benchmarking big data recommendation algorithms using Hadoop or Apache Spark Efficient and socio-aware recommendation approaches for bigdata networked systems Novel hybrid approaches for big data recommendations Deep generative models for recommender systems Recommendation algorithms for unstructured big data such as text, audio, image and video Deep segregation of plastic (DSP): segregation of plastic and nonplastic using deep learning Spatiotemporal recommendation with big geo-social networking data Recommender system for predicting malicious Android applications Security threats and their mitigation in big data recommender systems User’s privacy in recommendation systems applying online social network data: a survey and taxonomy Private entity resolution for big data on Apache Spark using multiple phonetic codes Deep learning architecture for big data analytics in detecting intrusions and malicious URL
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