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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  • @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.