sc 18: e3

Research Article

Performance Analysis on Popularity Based, Content Based and Collaborative Filtering Utilizing Recommendation Framework

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  • @ARTICLE{10.4108/eai.18-8-2020.166001,
        author={Deepkiran Munjal and Anju Gera and Pawan Kumar Singh},
        title={Performance Analysis on Popularity Based, Content Based and Collaborative Filtering Utilizing Recommendation Framework},
        journal={EAI Endorsed Transactions on Smart Cities: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SC},
        year={2020},
        month={8},
        keywords={Art education and media Recommender System, Popularity Based, Content Based filtering, Collaborative Filtering, smart cities, application of communication systems},
        doi={10.4108/eai.18-8-2020.166001}
    }
    
  • Deepkiran Munjal
    Anju Gera
    Pawan Kumar Singh
    Year: 2020
    Performance Analysis on Popularity Based, Content Based and Collaborative Filtering Utilizing Recommendation Framework
    SC
    EAI
    DOI: 10.4108/eai.18-8-2020.166001
Deepkiran Munjal1,*, Anju Gera2, Pawan Kumar Singh2
  • 1: Master of Computer Application, G. L Bajaj Institute of Technology and Management, Greater Noida, India
  • 2: Computer Science and Engineering, G. L Bajaj Institute of Technology and Management, Greater Noida, India
*Contact email: deepa.munjal@gmail.com

Abstract

In today's computerized world, it has become an irritating undertaking to locate the substance of one's loving in an interminable assortment of substance that are being devoured like art, education, media and so on. Then again there has been a developing development among the computerized substance suppliers who need to connect the same number of clients on their administration as feasible for the most extreme time. A tune proposal is significant in our public activity because of its highlights, for example, in building smart cities recommending a lot of melodies to clients dependent on their advantage, or the popularity of the tunes. In this paper we are proposing a song suggestion framework that can prescribe song to another client just as the other existing clients. We use Popularity Based, Content Based separating, and Collaborative Filtering, which is a blend of application of communication systems, to develop a framework that gives progressively exact proposals concerning melodies.