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Recommender systems, their approaches and challenges: a literature review

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dc.contributor.author Boralessa, G. W. Y. S.
dc.contributor.author Shafana, M. S.
dc.contributor.author Ragel, Roshan G.
dc.contributor.author Ahamed Sabani, M.
dc.date.accessioned 2022-07-06T10:47:49Z
dc.date.available 2022-07-06T10:47:49Z
dc.date.issued 2022-05-25
dc.identifier.citation Book of Abstracts - Proceedings of the 10th International Symposium 2022 on "Multidisciplinary Research for Encountering Contemporary Challenges”. 25th May 2022. South Eastern University of Sri Lanka, Oluvil, Sri Lanka. pp. 44. en_US
dc.identifier.isbn 978-624-5736-37-9
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/6171
dc.description.abstract Over the last few years, Recommender Systems (RS) have shown massive growth and become increasingly essential as web service giants like “youtube” and “Netflix” skyrocketed in terms of popularity., RS can be defined as algorithms that attempt to suggest relevant products to consumers. Collaborative filtering, content-based, and hybrid recommendation methods are the primary recommendation methods that will be discussed in this work. This paper also covers the basics and potential ways to increase the relevance and competence of RS and the limitations and constraints of the current recommendation approach, including the cold-start problem, stability vs plasticity problem, sparsity issues, etc. This study provides a comprehensive overview of current state-of-the-art Recommender System methods are utilized in several application areas. A systematic review was conducted using highly referenced literature discovered on Google Scholars then filtered down to the most current and relevant studies in the RS field. This study aims to provide researchers and industrial developers with a concise guide to recommender systems through a systematic analysis. en_US
dc.language.iso en_US en_US
dc.publisher South Eastern University of Sri Lanka, Oluvil, Sri Lanka. en_US
dc.subject Recommendation Approaches en_US
dc.subject Recommender Systems en_US
dc.subject Hybrid Recommenders en_US
dc.subject Review en_US
dc.subject Survey en_US
dc.title Recommender systems, their approaches and challenges: a literature review en_US
dc.type Article en_US


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