Summary As various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.

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Recently, with the development of mobile devices and near field communication, mobile P2P networks have been actively studied to improve the limits of the existing centralized processing system. A peer has limited components such as batteries, memory and storage spaces in mobile P2P networks. The trust of a peer should be discriminated in order to share reliable contents in mobile P2P networks. In this paper, we propose a trust discrimination scheme considering limited resources in mobile P2P environments. The proposed scheme discriminates the trust of a peer by direct rating values using the rating information of the peer and indirect rating values by the other peers. The recent update time is included in the rating information. The proposed scheme reduces the redundant rating information by comparing the recent update times of the rating information. It is shown through performance evaluation that the proposed scheme reduces the number of messages and improves the accuracy of trust over the existing scheme.■ keyword :|Mobile P2P|Peer|Trust|Content|Malicious Peer|