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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87604

    Title: Estimating trust value : a social network perspective
    Other Titles: 估算信任值 : 一個社會網絡的視角
    Authors: 狄愛林;Castillo, Arleen Nicole Diaz
    Contributors: 淡江大學企業管理學系碩士班
    張瑋倫;Chang, Wei-Lun
    Keywords: 信任;社會網絡;集群;自組織映射圖網路;網路建議系統;社會媒體;Trust;Social Network;Clustering;SOM;ORS;Social media
    Date: 2012
    Issue Date: 2013-04-13 11:28:35 (UTC+8)
    Abstract: 資訊負荷已成為現今資訊時代的一大問題,在資訊量成長快速的狀況下,資訊過
    Information overload is an increasing problem, and as information available
    continues to grow in volume, current filtering techniques are proving inefficient.
    Social network users and people in general, tend to prioritize recommendations
    coming from people they are acquainted to. The purpose of this study was to
    investigate if it was possible to measure trust within individuals in a social network,
    as well as find out if data clustering methods could help to achieve said goal. Another
    aim was to develop a trust model that would estimate a trust value for content creators
    on an online rating system with social network capabilities.
    This research introduces the concept of social distance, which is drawn from
    clustering methods applied to the social network user base; and incorporates said
    distance in the estimation of trust, as well as user generated ratings. The trust value
    estimated will serve as a metric for filtering and sorting content of any kind based on
    the trustworthiness of the creator.
    The results of the study revealed that it is possible to provide an estimate
    measure of trust within individuals in a social network and that clustering methods
    were of significant help into said evaluation as well as the integration of other
    variables affecting the building of trust. It was found that the model proposed by this
    study was able to integrate various variables and provide a more complete and
    integrated, multidimensional value to an estimated trust. Results also showed, that
    higher rating scores combined with shorter social distances provide satisfactory trust
    values, while the opposite happened for subjects presenting lower rating scores in
    combination with longer distances.
    The principal conclusion was that our model provides a multidimensional
    estimated value for trust on content from the Internet, that integrates some of the
    variables necessary for the building of trust in online setting, as are: social distance,
    weight of relationship, time, and ratings from an online rating system; as well trust
    levels between individuals within a social network. This study contributes to the
    current literature on trust estimation and social networks role in such endeavors. This
    will provide also an alternative for current information overload issues as well.
    Appears in Collections:[企業管理學系暨研究所] 學位論文

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