Mitigating to user cold-start issue in Recommendations System

Anurag Singh; Subhadra Shaw

Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India

Research Article

Mitigating to user cold-start issue in Recommendations System

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.16-4-2022.2318161,
    author={Anurag  Singh and Subhadra  Shaw},
    title={Mitigating to user cold-start issue in Recommendations System},
    proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India},
    publisher={EAI},
    proceedings_a={THEETAS},
    year={2022},
    month={6},
    keywords={recommend system popularity-based recommendation cold- start rmse},
    doi={10.4108/eai.16-4-2022.2318161}
}

Anurag Singh
Subhadra Shaw
Year: 2022
Mitigating to user cold-start issue in Recommendations System
THEETAS
EAI
DOI: 10.4108/eai.16-4-2022.2318161

Anurag Singh¹^,*, Subhadra Shaw¹

1: Department of Computer Sc. & App., AKS University , Satna(M.P.)

*Contact email: singhanurag.jbp@gmail.com

Abstract

This research paper uses a brand new popular model to resolve users' side issues within the recommendation process. the primary cold problem occurs when the target user doesn't have a rating history within the system. Today, the recommendation system has been utilized in various fields. However, they still suffer from various ailments, including cold sores and sparsity problems. The aim of the model is to recommend the highest five items to the user and therefore the performance of the model is evaluated.

Keywords: recommend system popularity-based recommendation cold- start rmse

Published: 2022-06-08
Publisher: EAI

: http://dx.doi.org/10.4108/eai.16-4-2022.2318161

Mitigating to user cold-start issue in Recommendations System

Abstract

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