This book combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools.

Chapter 1: Introduction to big data recommender systems - volume 1Chapter 2: Theoretical foundations for recommender systemsChapter 3: Benchmarking big data recommendation algorithms using Hadoop orApache SparkChapter 4: Efficient and socio-aware recommendation approaches for bigdata networked systemsChapter 5: Novel hybrid approaches for big data recommendationsChapter 6: Deep generative models for recommender systemsChapter 7: Recommendation algorithms for unstructured big data such as text, audio, image and videoChapter 8: Deep segregation of plastic (DSP): segregation of plastic and nonplastic using deep learningChapter 9: Spatiotemporal recommendation with big geo-social networking dataChapter 10: Recommender system for predicting malicious Android applicationsChapter 11: Security threats and their mitigation in big data recommender systemsChapter 12: User's privacy in recommendation systems applying online social network data: a survey and taxonomyChapter 13: Private entity resolution for big data on Apache Spark using multiple phonetic codesChapter 14: Deep learning architecture for big data analytics in detecting intrusions and malicious URL