A Method on Retrieving Personalized Information Based on Mutual Trust in Real and Online World


KIPS Transactions on Software and Data Engineering, Vol. 6, No. 5, pp. 257-266, May. 2017
10.3745/KTSDE.2017.6.5.257,   PDF Download:
Keywords: Trust Measure, Social Network, Personallized Information, Recommender System
Abstract

Two remarkable problems of recent online social network are information overflow and information overload. Since the mid-1990s, many researches to overcome these issues have been conducted with information recommender systems and context awareness based personalization techniques, the importance of trust or relationship between users to discover influential information has been increasing as recent online social networks become huge. But almost researches have not regarded trust or relationship in real world while reflecting them in online world. In this paper, we present a novel method how to discover influential and spreadable information that is highly personalized to a user. This valuable information is extracted from an information set that consists of lots of information user missed in the past, and we assumes important information is likely to exist in this set.


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Cite this article
[IEEE Style]
M. Kim and S. Kim, "A Method on Retrieving Personalized Information Based on Mutual Trust in Real and Online World," KIPS Transactions on Software and Data Engineering, vol. 6, no. 5, pp. 257-266, 2017. DOI: 10.3745/KTSDE.2017.6.5.257.

[ACM Style]
Myeonghun Kim and Sangwook Kim. 2017. A Method on Retrieving Personalized Information Based on Mutual Trust in Real and Online World. KIPS Transactions on Software and Data Engineering, 6, 5, (2017), 257-266. DOI: 10.3745/KTSDE.2017.6.5.257.