Contents Recommendation Scheme Considering Trust and Collaborative Filtering in Online Social Networks

Bok, Kyoungsoo; Geonsik, Ko; Lim, Jongtae; Lee, Keon Myung; Yoo, Jaesoo

doi:10.1145/3129676.3130217

Cited by 1 publication

(2 citation statements)

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“…With such a large amount of content being created, there is an increasing requirement for the users to select from all this information and identify the content that is appropriate for them . Mostly, research has been performed on content recommendation schemes for selectively providing users with only the content they prefer . For example, Netflix, the world's largest video streaming company, identifies users' preferences and recommends movies or TV programs that match their preferences by using information on viewing content, search history, and social data collected from Facebook and Twitter.…”

Section: Introductionmentioning

confidence: 99%

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Personalized content recommendation scheme based on trust in online social networks

Bok

Geonsik

Lim

et al. 2019

Concurrency and Computation

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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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Section: Introductionmentioning

confidence: 99%

“…We proposed a basic concept for supporting trust‐based content recommendation in previous work . This paper is an extended version of the existing schemes using user and content trust to improve the accuracy of recommendations based on collaborative filtering.…”

Section: Introductionmentioning

confidence: 99%