Abstract
Recommendation systems recommender systems are a subcategory of information filtering that is utilized to determine the preferences of users towards certain items. These systems emerged in the 1990’s and they have since changed the intelligence of both the web and humans. Vast amounts of research papers have been published in various domains. Recommendation systems suggest items to users and their principal purpose is to recommend items that are predicted to be suitable for users. Some of the most popular domains where recommendation systems are used include movies, music, jokes, restaurants, financial services, life insurance, Instagram Facebook and twitter followers. This paper explores different collaborative filtering algorithms. In so doing, the paper looks at the strengths and challenges (open issues) faced by this technique. The open issues give direction of future research work to researchers and also provide information of where to use collaborative filtering recommender systems applications.
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Manamolela, L., Zuva, T., Appiah, M. (2020). Collaborative Filtering Recommendation Systems Algorithms, Strengths and Open Issues. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_14
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