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Personalized Recommender System for Digital Libraries

Author

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  • Omisore M. O.

    (Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria)

  • Samuel O. W.

    (Department of Computer Science, Federal University of Technology Akure, Akure, Ondo, Nigeria)

Abstract

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start problem inherent to CBF is assuaged by cold start engine. An experimental study was carried out on a database of 10000 books from different categories of computing studies. The outcome tracked over a period of eight months shows that the proposed system induces greater user satisfaction and this attests users' desirability of the system.

Suggested Citation

  • Omisore M. O. & Samuel O. W., 2014. "Personalized Recommender System for Digital Libraries," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 9(1), pages 18-32, January.
    • Handle: RePEc:igg:jwltt0:v:9:y:2014:i:1:p:18-32
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijwltt.2014010102
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    Cited by:

    1. Haytham Karar, 2019. "Algorithmic Capitalism and the Digital Divide in Sub-Saharan Africa," Journal of Developing Societies, , vol. 35(4), pages 514-537, December.
    2. Iurie BADICU, 2022. "The Influence Of The Digital Economy Development On The Global Music Industry Production Regarding The Harmonization Of Accounting In Brics Emerging Economies," European Journal of Accounting, Finance & Business, "Stefan cel Mare" University of Suceava, Romania - Faculty of Economics and Public Administration, West University of Timisoara, Romania - Faculty of Economics and Business Administration, vol. 10(1), pages 21-29, February.
    3. Ojokoh B.A & Olayemi O.C & Babalola A.E & Eyo E.O, 2018. "A User-Centric Housing Recommender System," Information Management and Business Review, AMH International, vol. 10(3), pages 17-24.

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