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首页> 外文期刊>Signal Processing Magazine, IEEE >In Tags We Trust: Trust modeling in social tagging of multimedia content
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In Tags We Trust: Trust modeling in social tagging of multimedia content

机译:在我们信任的标签中:多媒体内容社交标签中的信任建模

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摘要

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.
机译:如今,在线社交网络中的标签非常流行,因为它有助于多媒体内容的搜索和检索。但是,嘈杂和垃圾邮件注释通常使执行高效搜索变得困难。用户可能会在添加标签时出错,并且使用不相关的标签,并且可能会恶意添加内容以进行广告或自我宣传。本文概述了在社交标签中消除此类噪音和垃圾邮件的技术的最新进展。我们将最新方法分类为几类,并分别研究代表性示例。我们还将对它们进行定性比较和对比,并概述未解决的问题,以备将来研究之用。

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