FunkR-pDAE: Personalized Project Recommendation Using Deep Learning
Download as DOCX, PDF0 likes84 views
The paper introduces funkr-pdae, a personalized project recommendation system for developers in open source communities, utilizing deep learning to overcome limitations of existing methods. It builds relevance matrices and applies deep auto-encoders to offer personalized recommendations with experimental results showing a precision rate of 75.46% and a recall rate of 40.32%. The study highlights its practical significance in efficiently matching developers to suitable projects based on their skills and behavior.
FunkR-pDAE: Personalized Project Recommendation Using Deep Learning
- 1. FunkR-pDAE: Personalized Project Recommendation Using Deep Learning ABSTRACT: In open source communities, developers always need to spend plenty of time and energy on discovering specific projects from massive open source projects. Consequently, the study of personalized project recommendation for developers has important theoretical and practical significance. However, existing recommendation approaches have clear limitations, such as ignoring developers’ operating behavior, social relationships and practical skills, and are very inefficient for large amounts of data. To address these limitations, this paper proposes FunkR- pDAE (Funk singular value decomposition Recommendation using pearson correlation coefficient and Deep Auto-Encoders), a novel personalized project recommendation approach using a deep learning model. FunkR-pDAE first extracts data related to developers and open source projects from open source communities, which build a developer-open source project relevance matrix and a developer-developer relevance matrix. Meanwhile, Pearson Correlation Coefficient is utilized to calculate developer similarity using the developer-developer relevance matrix. Second, deep auto-encoders are used to learn the factor vectors that represent developers and open source projects. Finally, a sorting method is defined to provide personalized project recommendations. Experimental results on real-world GitHub data sets show that FunkR-pDAE has a precision rate of 75.46% and a recall rate of 40.32%, which provides more effective recommendation compared with state-of-the-art approaches.
- 2. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium Dual Core. Hard Disk : 120 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 1 GB SOFTWARE REQUIREMENTS: Operating system : Windows 7. Coding Language : Python Database : MYSQL REFERENCE: Pengcheng Zhang, Fang Xiong, Hareton Leung, and Wei Song, “FunkR-pDAE: Personalized Project Recommendation Using Deep Learning”, IEEE Transactions on Emerging Topics in Computing, 2019.