Recommendation systems: Principles, methods and evaluation
By Folasade Olubusola Isinkaye, Yetunde Folajimi, Bolanle Adefowoke Ojokoh. 2015
1,299 citations recorded by the source corpus.
Abstract On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/634204148a74e377770dab9a15c9f52ff26d3cbe
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In Computer Networks and Communications
In Information Systems
Citations in the corpus
- Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “One of the benefits of the CF models over CB approaches is the ability to work in a domain where the content associated items are insufficiently available and in a situation where contents are difficult to process such as opinions (Isinkaye et al. 2015).”
- A systematic review and research perspective on recommender systems (2022, background): “This problem arises when similar or related items have different entries or names, or when the same item is represented by two or more names in the system [78].”