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Recommender Systems

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Shuai Zhang

    (University of New South Wales)

  • Aston Zhang

    (Amazon)

  • Lina Yao

    (University of New South Wales)

Abstract

Recommender systems have achieved widespread success in real-life applications. Personalized recommendation can reduce customers’ effort in finding items they are interested in. It is also critical in some industries as it can increase customer stickiness and help industries to stand out from competitors. Recommender systems made a significant progress over the last decade, and the advancements are fruitful and inspiring. Given its importance, this chapter aims at introducing the fundamentals and advances of recommender systems. In specific, we will present readers with the widely used techniques, applications, and evaluation methods of recommender systems, in the hope that it could help them to get a thorough and clear understanding to this field.

Suggested Citation

  • Shuai Zhang & Aston Zhang & Lina Yao, 2023. "Recommender Systems," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 637-658, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_28
    DOI: 10.1007/978-3-031-24628-9_28
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