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A global user profile framework for effective recommender systems

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posted on 2023-12-04, 14:22 authored by Loubna Mekouar, Youssef Iraqi, Issam DamajIssam Damaj

Modern Recommender Systems (RSs) compete to maintain rich user profiles that can accurately reflect user behavior, interests, and service contexts. While benefiting from an online service supported by an RS, user preferences and interests may rapidly change over time. To keep up with the changes from the user perspective, an RS should maintain the making of effective personalization as supported by robust profile construction methods. Building an effective user profile database requires exhaustive data and behavior analysis over extended periods. In this paper, we delve into traditional RS architectures to identify limitations, gaps, and opportunities for improvements in existing user profile mechanisms. To that end, a Global User Profile Framework (GUPF) is proposed towards achieving increased effectiveness. Furthermore, the adoption of the developed framework is exemplified by presenting different potential scenarios. The presented work concludes with the identification of important venues and research directions that are enabled by the proposed GUPF.

History

Publisher

Springer

Version

  • VoR (Version of Record)

Citation

Mekouar, L., Iraqi, Y., & Damaj, I. (2023) 'A global user profile framework for effective recommender systems', Multimedia Tools and Applications, 1-21. https://doi.org/10.1007/s11042-023-17436-w

Print ISSN

1380-7501

Electronic ISSN

1573-7721

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Issam Damaj

Copyright Holder

  • © The Authors

Language

  • en

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