Dataset Information

A collaborative approach for research paper recommender system.

ABSTRACT: Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

SUBMITTER: Haruna K

PROVIDER: S-EPMC5628815 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

A collaborative approach for research paper recommender system.

Haruna Khalid K Akmar Ismail Maizatul M Damiasih Damiasih D Sutopo Joko J Herawan Tutut T

PloS one 20171005 10

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be fr ...[more]

PMID: 28981512

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Project description:Background: The Rare Disease Research Partnership (RAinDRoP) was established in 2018 to bring together a wide variety of diverse voices in the rare disease community in Ireland and form a research partnership. This approach enabled clinicians, patients, carers and researchers to work together to identify top research priorities for rare diseases, which focused on a life-course perspective rather than a disease-specific need. Methods: A participatory multiple phase approach was used to identify research priorities for rare diseases. The research process involved three main phases: Phase I, Public Consultation Survey on Research in Rare Diseases in Ireland (PCSRRDI); Phase II, Research Prioritisation Workshop (RPW); Phase III, Follow-up Public Consultation and Prioritisation Survey (FWPCPS). Results: In total, 240 individuals completed the phase I PCSRRDI, which comprised of a cross-section of health care professionals, researchers and people living with rare diseases. One thousand and fifteen statements were collected, reflecting issues and shared challenges in rare diseases. A shortlisting step by step was used to identify any statements that had received a total score of above 50% into 10-12 researchable questions or statements per the theme for the phase II workshop. Phase II was focused on three main themes: (1) Route to Diagnosis, (2) Living with Rare Disease, (3) Integrated and Palliative Care. In total, 62 individuals attended the overall workshop; 42 participated in the prioritisation sessions. A cross-section of health care professionals, researchers and people living with rare diseases were engaged at each workshop. Seventy-five individuals completed the final phase III public ranking by priority responses, and they ranked the top 15 research priorities defined by the multi-stakeholders at the phase II consensus meeting. Conclusions: This study identified priorities for rare diseases research aimed at improving the health and wellbeing of people living with rare diseases.

Dataset Information

A collaborative approach for research paper recommender system.

Publications

A collaborative approach for research paper recommender system.

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