Hybridrec: A Recommender System for Tagging Github Repositories

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Hybridrec: A Recommender System for Tagging Github Repositories 49 Pages Posted: 16 Dec 2021 See all articles by Juri Di RoccoJuri Di Roccoaffiliation not provided to SSRNDavide Di Ruscioaffiliation not provided to SSRNClaudio Di Sipioaffiliation not provided to SSRNPhuong Than Nguyenaffiliation not provided to SSRNRiccardo Rubeiaffiliation not provided to SSRN Abstract Software repositories are increasingly essential to support the management of typical artifacts building up projects, including source code, documentation, and bug reports. GitHub is at the forefront of this kind of platforms, providing developers with a reservoir of code contained in more than 28M repositories. To help developers find the right artifacts, GitHub uses topics, which are short texts assigned to the stored artifacts. However, assigning inappropriate topics to a repository might hamper its popularity and reachability. We implemented MNBN and TopFilter to recommend GitHub topics in our previous work. MNBN exploits a stochastic network to predict topics, while TopFilter relies on a syntactic-based function to suggest topics. This paper extends our work by building HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics. We employ a Complement Naïve Bayesian Network (CNBN) to deal with unbalanced datasets. We apply a preprocessing phase to clean and refine the input data before feeding the recommendation engine. An empirical evaluation demonstrates that HybridRec outperforms three state-of-the-art baselines, obtaining a better performance with respect to various metrics. We conclude that the conceived framework can help developers increase their projects' visibility. Keywords: Recommender systems, Github topics recommendations, Collaborative-Filtering techniques, Naïve Bayesian Network Suggested Citation: Suggested Citation Di Rocco, Juri and Di Ruscio, Davide and Di Sipio, Claudio and Nguyen, Phuong Than and Rubei, Riccardo, Hybridrec: A Recommender System for Tagging Github Repositories. Available at SSRN: https://ssrn.com/abstract=3986698 Juri Di Rocco affiliation not provided to SSRN ( email ) No Address Available Davide Di Ruscio affiliation not provided to SSRN ( email ) No Address Available Claudio Di Sipio (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Phuong Than Nguyen affiliation not provided to SSRN ( email ) No Address Available Riccardo Rubei affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place job opening Paper statistics Downloads 2 Abstract Views 2 PlumX Metrics Related eJournals Information Systems & Economics eJournal Follow Information Systems & Economics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 240 PAPERS 13,536 This Journal is curated by: Erik Brynjolfsson at National Bureau of Economic Research (NBER) Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.253 seconds