Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/6171
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dc.contributor.authorBoralessa, G. W. Y. S.-
dc.contributor.authorShafana, M. S.-
dc.contributor.authorRagel, Roshan G.-
dc.contributor.authorAhamed Sabani, M.-
dc.date.accessioned2022-07-06T10:47:49Z-
dc.date.available2022-07-06T10:47:49Z-
dc.date.issued2022-05-25-
dc.identifier.citationBook 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.isbn978-624-5736-37-9-
dc.identifier.urihttp://ir.lib.seu.ac.lk/handle/123456789/6171-
dc.description.abstractOver 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.isoen_USen_US
dc.publisherSouth Eastern University of Sri Lanka, Oluvil, Sri Lanka.en_US
dc.subjectRecommendation Approachesen_US
dc.subjectRecommender Systemsen_US
dc.subjectHybrid Recommendersen_US
dc.subjectReviewen_US
dc.subjectSurveyen_US
dc.titleRecommender systems, their approaches and challenges: a literature reviewen_US
dc.typeArticleen_US
Appears in Collections:10th International Symposium - 2022

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