Abstract
In the current context, characterized by an information overload, also known as infobesity, it has become essential to design mechanisms that allow users to access what interests them as quickly as possible. Hence, recommender systems have emerged. This article then consists of examining and comparing the different existing recommendation approaches: those content-based filtering, those collaborative filtering, and finally the demographic and social approaches, while indicating, for each approach, the fields of application, some interesting examples, as well as its advantages and limitations. Then, we will indicate the hybridization techniques available to overcome these limitations.
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Notes
- 1.
Cold start refers to the lack of information on a new user or a new item that has just been added to the recommendation system.
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Al Fararni, K., Aghoutane, B., Riffi, J., Sabri, A., Yahyaouy, A. (2020). Comparative Study on Approaches of Recommendation Systems. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_72
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