Danielle Lee, Nak-Jun Sung, Jung-Gyu Lee, Jin Wook Choi, Seung-Ryul Jeong, Chul-Hyun Kim, Ahyoung Lee, Min Hong, Journal of Internet Computing and Services, Vol. 18, No. 4, pp. 7-18, Aug. 2017
10.7472/jksii.2017.18.4.07, Full Text:  HTML
Keywords: Online Social Network, Information Similarity, Social Structure, Homophily, Badminton Training, Motion Analysis System, IoT sensor, Agility Test, U-Art Communication, Project Management, Performance Measurement, Bipolar Diagram, Dashboard

Abstract

Despite various types of online social networks having different topological and functional characteristics, the kinds of online social networks considered in social recommendations are highly restricted. The pervasiveness of social networks has brought scholarly attention to expanding the scope of social recommendations into more diverse and less explored types of online social networks. As a preliminary attempt, this study examined the information-sharing patterns of a new type of online social network - unilateral (directed) network - and assessed the feasibility of the network as a useful information source. Specifically, this study mainly focused on the presence of shared interests in unilateral networks, because the shared information is the inevitable condition for utilizing the networks as a feasible source of personalized recommendations. As the results, we discovered that user pairs with direct and distant links shared significantly more similar information than the other non-connected pairs. Individual users' social properties were also significantly correlated with the degree of their information similarity with social connections. We also found the substitutability of online social networks for the top cohorts anonymously chosen by the collaborative filtering algorithm.

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