PROMPT: Personalized User Tag Recommendation for Social Media Photos Leveraging Personal and Social Contexts
PROMPT: Personalized User Tag Recommendation for Social Media Photos Leveraging Personal and Social Contexts
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DOI：
10.1109/ism.2016.0109
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
R. Shah;Anupam Samanta;Deepak Gupta;Yi Yu;Suhua Tang;Roger Zimmermann
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文献类型：
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作者：
R. Shah;Anupam Samanta;Deepak Gupta;Yi Yu;Suhua Tang;Roger Zimmermann
Flickr等社交媒体平台允许用户使用描述性关键字（称为标签）对照片进行注释，目的是使多媒体内容易于理解、搜索和浏览。然而，手动标注对于大多数用户来说非常耗时和繁琐，这使得搜索相关照片变得困难。此外，照片的预测标签不一定与用户的兴趣相关。因此，需要一种自动标签预测系统，该系统考虑用户的兴趣并描述照片的客观方面，例如视觉内容和活动。为此，本文提出了一个标签推荐系统，称为，PROMPT，推荐个性化的标签为一个给定的照片利用个人和社会背景。具体来说，首先，我们确定一组与照片的用户有相似标签行为的用户，这在推荐个性化标签时非常有用。接下来，我们从视觉内容、文本元数据和相邻照片的标签中找到候选标签，并推荐五个最合适的标签。我们使用非对称标签共现概率初始化候选标签的分数和邻居投票后的标签的归一化分数，然后执行随机游走，以促进有许多近邻的标签和削弱孤立的标签。最后，我们为给定的照片推荐前五个用户标签。在Flickr数据集（测试集中有46，700张照片，训练集中有2800万张照片）上的实验结果证实了所提出的算法优于最先进的算法。
Social media platforms such as Flickr allow users to annotate photos with descriptive keywords, called, tags with the goal of making multimedia content easily understandable, searchable, and discoverable. However, manual annotation is very time-consuming and cumbersome for most users, which makes it difficult to search relevant photos. Moreover, predicted tags for a photo are not necessarily relevant to users' interests. Thus, it necessitates for an automatic tag prediction system that considers users' interests and describes objective aspects of the photo such as visual content and activities. To this end, this paper presents a tag recommendation system, called, PROMPT, that recommends personalized tags for a given photo leveraging personal and social contexts. Specifically, first, we determine a group of users who have similar tagging behavior as the user of the photo, which is very useful in recommending personalized tags. Next, we find candidate tags from visual content, textual metadata, and tags of neighboring photos, and recommends five most suitable tags. We initialize scores of the candidate tags using asymmetric tag co-occurrence probabilities and normalized scores of tags after neighbor voting, and later perform random walk to promote the tags that have many close neighbors and weaken isolated tags. Finally, we recommend top five user tags to the given photo. Experimental results on a Flickr dataset (46,700 photos in the test set and 28 million photos in the train set) with 1,540 unique user tags confirm that the proposed algorithm outperforms state-of-the-arts.