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