Sakib, Nazmus and Ahmad, Rodina and Ahsan, Mominul and Based, Md Abdul and Haruna, Khalid and Haider, Julfikar and Gurusamy, Saravanakumar (2021) A hybrid personalized scientific paper recommendation approach integrating public contextual metadata. IEEE Access, 9. pp. 83080-83091. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3086964.
Full text not available from this repository.Abstract
Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Metadata; Collaborative filtering; Filtering; Feature extraction; Context modeling; Recommender systems; Computer science; Scientific paper recommendation; Public contextual metadata; Content-based filtering; Collaborative filtering; Hybrid approach |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Software Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 17 Aug 2022 07:31 |
| Last Modified: | 17 Aug 2022 07:31 |
| URI: | http://eprints.um.edu.my/id/eprint/28642 |
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