Dynamic Reputation Rating Mechanism for Social Content Curation Services

Abstract

Recently various social curation mechanisms have been developed to organize and suggest digital contents around one or more particular themes or topics for online users on Social Network Services (SNS). Collaborative filtering method can be used to improve efficiency of automated social curation systems, and so we have already applied this method to enhance credibility of curators in previous research, but these approaches have problem in extracting user preferences for users who have not evaluated many contents. In this study, we use dynamic curator groups which are automatically formed to recommend and organize domain specific contents. The group members have dynamic reputation value depending on their evaluation performance. Social curations over online digital contents are very effective to find relevant information in a specific domain. In addition, we show simulation results to evaluate the reliability enhancement of the proposed dynamic curator model for automated curation services of social content.