Improving recommendations utilizing users’ demographic information

Avick Kumar Dey (), Pijush Kanti Dutta Pramanik (), Pradeep Kumar Singh () and Prasenjit Choudhury ()
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Avick Kumar Dey: DSMS College
Pijush Kanti Dutta Pramanik: Galgotias University
Pradeep Kumar Singh: Galgotias University
Prasenjit Choudhury: National Institute of Technology Durgapur

Quality & Quantity: International Journal of Methodology, 2024, vol. 58, issue 6, No 24, 5559-5575

Abstract: Abstract The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset.

Keywords: Recommendation system; Collaborative filtering; Demographic information; MAE; RMSE (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11135-024-01890-1

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