A recommendation method for social collaboration tasks based on personal social preferences

In the social-collaboration scenario, the social-collaboration tasks need to be completed/coordinated by multiple people. For example, in the field of open-source software development, most software developments require multiple developers to collaborate with each other. With the increase in the number and variety of social-collaboration tasks, it is difficult for individuals to discover social-collaboration tasks that they can participate. If we can help match the social-collaboration tasks with appropriate users, the quality and speed of these tasks will be improved, thus social organizations (e.g., companies, teams, and research institutions) and individuals can improve productivity, which is very significant. However, most related work in recommending individuals to participate in social-collaboration tasks mainly focus on individual's data features/characteristics (e.g., personal behaviors and attributes), and little work is based on the social collaborative data features/characteristics within the individual's participation in the social-collaboration tasks, such as the types of social-collaboration tasks that individuals participate, collaborative content, collaborative behavior, collaborative intensity, and so on. This paper proposes a universal recommendation method based on personal social-collaboration preferences, which is used to recommend the social-collaboration tasks that individuals can participate. The characteristics of social-collaboration data contain a large number of individuals' preferences for social-collaboration, which helps improve recommendation performance. We perform a large number of experiments to verify the effectiveness of our proposed method, based on our collected real-world data sets in the open source software development services (Bugzilla and Github). Experimental results show that the proposed algorithm can be well applied to the social-collaboration task recommendation.