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  3. Recommender Systems
  • Cited by 748
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    • Publisher:
      Cambridge University Press
      Publication date:
      05 August 2012
      30 September 2010
      ISBN:
      9780511763113
      9780521493369
      DOI:
      https://doi.org/10.1017/CBO9780511763113
      Dimensions:
      (228 x 152 mm)
      Weight & Pages:
      0.6kg, 352 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Knowledge Management, Databases and Data Mining, Computer Science
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    Subjects:
    Knowledge Management, Databases and Data Mining, Computer Science
    Information

    Book description

    In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

    Reviews

    ‘Behind the modest title of ‘An Introduction’ lies the type of work the field needs to consolidate its learning and move forward to address new challenges. Across the chapters that follow lie both a tour of what the field knows well - a diverse collection of algorithms and approaches to recommendation - and a snapshot of where the field is today as new approaches derived from social computing and the semantic web find their place in the recommender systems toolbox. Let's all hope this worthy effort spurs yet more creativity and innovation to help recommender systems move forward to new heights.’

    Joseph A. Konstan - from the Foreword

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