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 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