FunkR-pDAE: Personalized Project Recommendation Using Deep Learning To buy this project in ONLINE, Contact: Email: jpinfotechprojects@gmail.com, Website: https://www.jpinfotech.org

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.