Boosting social collaborations based on contextual synchronization: An empirical study

Supporting context-based collaboration among online users is an important issue to computer-mediated collaboration to fulfill specified tasks. However, several problems make it difficult to be aware of the context. The context of the user task can be (i) dynamic (i.e., changing over time), and (ii) mixed with multiple sub-contexts together. We propose a novel ontology-based platform to overcome these problems. It finds the most relevant users from a given social network, taking into account two types of context (i.e., personal and group contexts) and matching them. By measuring similarities between the personal contexts, we can dynamically organize a number of communities, so that users can be contextually synchronized. Individual users can be involved in complex collaborations related to multiple semantics. This paper demonstrates and discusses how the proposed context synchronization process is able to boost social collaborations. We show the experimental results collected from a collaborative information searching system. The main empirical issues in this work are (i) setting thresholds, (ii) searching performance, and (iii) scalability testing.