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Trust Aware Recommender System for Social Coding Platforms (GitHub Case Study)

dc.contributor.authorNkoro, Joseph Ahamefula
dc.description.abstractSocial networking systems have found their way into all sectors of life. With the advent of social coding platform like GitHub, networks of developers can be inferred based on the projects they participated in. When a new project is created by a developer on such social coding platforms, these platforms lack the capacity to recommend potential collaborators. Recommender systems are software techniques and tools that give item suggestions to users who might be interested in such an item. Having identified this problem, we developed ProjectTrust, a trust-aware recommender model which evaluates trust between projects and developers. A natural language processing approach was identified to be a good tool for text feature extraction in GitHub readme files. As the verification of the proposed framework, experiments using real social data from GitHub are presented and results show the effectiveness of the proposed approach.en_US
dc.description.sponsorshipAUST and AfDB.en_US
dc.subject2017 Computer Science and Engineering Thesesen_US
dc.subjectNkoro Joseph Ahamefulaen_US
dc.subjectRecommender Systemsen_US
dc.subjectNatural Language Processingen_US
dc.subjectTerm Frequencyen_US
dc.subjectInverse Document Frequencyen_US
dc.subjectDr. Victor Odumuyiwaen_US
dc.titleTrust Aware Recommender System for Social Coding Platforms (GitHub Case Study)en_US

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  • Computer Science105

    This collection contains Computer Science Student's Theses from 2009-2022

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