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Introduction to big data recommender systems — volume 1

Introduction to big data recommender systems — volume 1

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In the past few years, numerous recommendation approaches have been proposed to address various challenges of recommender systems. However, there are still many open and unresolved issues that require novel and more efficient recommendation solutions to handle big data. The book Big Data Recommender Systems: Recent Trends and Advances consists of two comprehensive volumes. Each volume consists of good quality chapters contributed by world renowned researchers and domain experts. Volume 1 aims 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.

Chapter Contents:

  • 1.1 Background
  • 1.2 About the book
  • Acknowledgments
  • References

Inspec keywords: security of data; learning (artificial intelligence); parallel algorithms; recommender systems; social networking (online); Big Data; data privacy

Other keywords: big data recommender systems; unstructured big data recommendations; MapReduce; machine learning; social recommendations; deep learning-based techniques; security; recommendation algorithm benchmarking; cyberattacks; geo-social recommendations; privacy; hybrid approaches

Subjects: Social and behavioural sciences computing; Knowledge engineering techniques; Parallel software; Information networks; Data handling techniques; Search engines

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