Recommender Systems

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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.

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APA

Zhang, S., Zhang, A., & Yao, L. (2023). Recommender Systems. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition (pp. 637–658). Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_28

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