Trust Aware Recommender System for Social Coding Platforms.
ABSTRACT
Social 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.
TABLE OF CONTENTS
CERTIFICATION……… ii
ABSTRACT…………… v
ACKNOWLEDGEMENTS…… vi
DEDICATION………………….. vii
TABLE OF FIGURES…………… x
LIST OF TABLES……………. x
CHAPTER ONE……… INTRODUCTION OF CONCEPT
- INTRODUCTION………… 1
- SOCIAL CODING AND VERSION CONTROL SYSTEMS………………. 1
- TRUST 3
- TRUST IN PSYCHOLOGY…………… 3
- TRUST IN SOCIOLOGY………….. 4
- TRUST IN COMPUTER SCIENCE…………… 4
- CHARACTERISTICS OF TRUST…… 5
- RECOMMENDER SYSTEMS…………. 7
- PROBLEM STATEMENT………….. 8
- OBJECTIVE OF THE RESEARCH WORK….. 8
- RESEARCH METHODOLOGY……. 9
- SCOPE OF WORK………. 9
- ORGANISATION OF WORK…….. 9
CHAPTER TWO……… LITERATURE REVIEW
- RECOMMENDER SYSTEM……………………. 10
- FUNCTIONS OF RECOMMENDER SYSTEM……… 11
- ELEMENTARY STRUCTURE OF RECOMMENDER SYSTEM…………… 12
- PERSONALIZED RECOMMENDATION…………….. 13
- COLLABORATIVE FILTERING RECOMMENDATION………… 13
- CONTENT BASED RECOMMENDER SYSTEMS………….. 16
- KNOWLEDGE BASE RECOMMENDATION SYSTEMS……… 17
- HYBRID RECOMMENDATION SYSTEMS…………… 18
- TRUST REPRESENTATION AND TRUST METRIC……….20
- TRUST MODELS………………. 22
- STATE OF THE ART ON RECOMMENDATION OF REPOSITORIES ON GitHub 31
CHAPTER THREE…… RESEARCH METHODOLOGY
- GitHub API EXPLORATION FOR DATA EXTRACTION……….. 33
- MAKING A GitHub API CONNECTION………………….. 34
- TEXT FEATURE EXTRACTION………… 36
- PROPOSED ALGORITHM….. 39
PHASE 1:….
3.3.1 39
- PHASE 2 40
- PHASE 3 41
- PHASE 4 43
- PHASE 5 44
CHAPTER FOUR……. EXPERIMENTATION AND RESULT
- WORD CLOUD VISUALIZATION OF README FILE SIMILARITY…………. 47
- RECOMMENDATION EVALUATION……………… 49
- CORRELATION BETWEEN TRUSTED DEVELOPERS WITH PROGRAMMING LANGUAGES AND WORK EXPERIENCE………………… 50
CHAPTER FIVE……… SUMMARY, RECOMMENDATION AND CONCLUSION
- SUMMARY…………….. 53
- CONCLUSION…….. 53
- FUTURE WORK…………… 54
REFERENCES 55
INTRODUCTION
With the growth of web technology, there has been an explosive growth in the size of content available on the Internet, social network interaction has exploded as well and has become a regular part of people’s life.
Other social lives activities like buying and selling now have a place to fit into social networks.
Researchers and scholars need information and resources from on-line document repositories and digital libraries for proper conducting of research work and they also require collaborative researches; casual chatting and communication via mails are also parts of the exploits made from advances in web technology.
The Web has turned to the best medium for many database applications, like e-commerce and digital libraries.
Many of these applications have even extended their functionalities by making use of APIs (Application Programming Interfaces).
REFERENCES
Abderrahim, N., & Benslimane, S. M. (2015). Towards Improving Recommender System: A Social Trust-Aware Approach. International Journal of Modern Education and Computer Science, 7(2), 8–15. https://doi.org/10.5815/ijmecs.2015.02.02
Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions, 17(6), 734–749.
Bouraga, S., Jureta, I., Faulkner, S., & Herssens, C. (2014). Knowledge-Based Recommendation Systems. International Journal of Intelligent Information Technologies, 10(2), 1–19. https://doi.org/10.4018/ijiit.2014040101
Burke, R. (2014). Hybrid Recommender Systems: Survey and Experiments. Retrieved from https://pdfs.semanticscholar.org/5880/b9bc3f75f4649b8ec819c3f983a14fca9927.pdf
Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. Springer. Retrieved from https://pdfs.semanticscholar.org/5880/b9bc3f75f4649b8ec819c3f983a14fca9927.pdf
Burke, R. (2003). Knowledge-based recommender systems. Researchgate. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.6029&rep=rep1&type=pdf