Browsing by Author "Silva, ATP"

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    A Novel approach for personalized article recommendation in online scientific communities
    (2015-06-09) Sun, J; Ma, J; Liu, X; Liu, Z; Wang, G; Jiang, H; Silva, ATP
    Rapid proliferation of information technologies has generated sheer volume of information which makes scientific research related information searching more challenging. Personalized recommendation is the widely adopted technique to recommend relevant documents to researchers. Current methods are suffering from mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure the relevance of the articles to researchers. The keyword similarity is used to generate candidate list of articles, and the journal similarity and author similarity are used to select most suitable articles from the candidate list. The integrated similarity measure is used to rank the articles based on their relevance. The proposed method is implemented in Scholar Mate (www.scholarmate.com), the online research social network platform. The evaluation results exhibit that proposed method is more effective than existing ones.
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    Personal loan default prediction and impact analysis of debt-to-income ratio
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Rodrigo, KLS; Sandanayake, TC; Silva, ATP; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    Loan defaults affect the financial sector, particularly impacting banks and lending institutions, resulting in a rise of non-performing assets and financial strain. To counteract this trend, traditional credit assessments use methods like credit scores and exploitation of socio-demographic composition of the customers. However, customers may possess numerous debt obligations that credit bureaus uncover, which can help to measure their repayment ability. This study proposed a comparative methodology that leverages five machine learning algorithms to predict personal loan defaults using debt-to-income ratio apart from the credit scoring models that prevail at banks. It analyzed the impact of debt payments on loan defaults and applied ensemble clustering to categorize customers’ risk levels based on their debt-to-income ratio. Experimental results indicated that ensemble clustering has enhanced the prediction power compared to conventional classification models to predict loan defaults.
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    item: Article-Abstract
    A Profile boosted RAF to Recommend Journals for Manuscript
    (2014-08-14) Silva, ATP; Ma, J; Yang, C; Liang, H
    With the increasing pressure on researchers to produce scientifically rigorous and relevant research, researchers need to find suitable publication outlets with the highest value and visibility for their manuscripts. Traditional approaches for discovering publication outlets mainly focus on manually matching research relevance in terms of keywords as well as comparing journal qualities, but other research-relevant information such as social connections, publication rewards, and productivity of authors are largely ignored. To assist in identifying effective publication outlets and to support effective journal recommendations for manuscripts, a three-dimensional profile-boosted research analytics framework (RAF) that holistically considers relevance, connectivity, and productivity is proposed. To demonstrate the usability of the proposed framework, a prototype system was implemented using the ScholarMate research social network platform. Evaluation results show that the proposed RAF-based approach outperforms traditional recommendation techniques that can be applied to journal recommendations in terms of quality and performance. This research is the first attempt to provide an integrated framework for effective recommendation in the context of scientific item recommendation.