Repurpose Social Network Sites based on LmPR: Like-minded People Recommendation

Abstract

The authors look at learning potentials of Social Network Sites (SNSs) and attempt to harness the wisdom of crowds. To promote interchanges of useful information among individuals and groups, this paper presents a model of a novel application, Like-minded People Recommendation (LmPR). LmPR recommends like-minded people to users in SNSs automatically based on text mining and collaborative filtering technologies. Hence, LmPR is able to help people to locate latent learning partners with similar interests and concerns. The learning partners consist of multiple roles such as novices, peers, and experts. To investigate the learning potential, the authors will implement LmPR on Twitter and will aim to assist and facilitate practices of Social Development Theory (SDT) and Collaborative Learning (CL) in SNSs. Through the use of LmPR, people are encouraged to spontaneously form a Community of Practice (CoP) to enhance the interactions among their learning community formed by like-minded people.