Hashtag Recommendation for Photo Sharing Services
Hashtag Recommendation for Photo Sharing Services
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DOI：
10.1609/aaai.v33i01.33015805
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Suwei Zhang;Yuan Yao;F. Xu;Hanghang Tong;Xiaohui Yan;Jian Lu
中科院分区：
文献类型：
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作者：
Suwei Zhang;Yuan Yao;F. Xu;Hanghang Tong;Xiaohui Yan;Jian Lu
标签可以极大地促进内容导航，提高用户在社交媒体中的参与度。尽管可能很有意义，但由于以下两个原因，为Instagram和Pinterest等照片共享服务推荐标签仍然是一项艰巨的任务。在内源性方面，照片共享服务中的帖子通常包含图像和文本，它们可能彼此相关。因此，对图像和文本以及它们之间的交互进行连贯的建模至关重要。在外生方面，主题标签是由用户生成的，不同的用户可能会因为他们不同的偏好和/或社区效应而为类似的帖子提出不同的标签。因此，非常希望表征用户的标记习惯。在本文中，我们提出了一个完整的和有效的标签推荐方法的照片共享服务。特别是，所提出的方法考虑了内容建模模块和习惯建模模块的内源性和外源性的影响，分别。对于内容建模模块，我们采用并行的共同注意力机制，连贯地建模图像和文本以及它们之间的交互;对于习惯建模模块，我们引入了一个外部存储单元来表征每个用户的历史标记习惯。基于来自内容建模模块的帖子特征和来自习惯建模模块的习惯影响两者来生成总体主题标签推荐。我们在真实的Instagram数据上评估了所提出的方法。实验结果表明，所提出的方法显着优于国家的最先进的方法在推荐准确性方面，内容建模和习惯建模的整体推荐准确性作出了显着贡献。
Hashtags can greatly facilitate content navigation and improve user engagement in social media. Meaningful as it might be, recommending hashtags for photo sharing services such as Instagram and Pinterest remains a daunting task due to the following two reasons. On the endogenous side, posts in photo sharing services often contain both images and text, which are likely to be correlated with each other. Therefore, it is crucial to coherently model both image and text as well as the interaction between them. On the exogenous side, hashtags are generated by users and different users might come up with different tags for similar posts, due to their different preference and/or community effect. Therefore, it is highly desirable to characterize the users’ tagging habits. In this paper, we propose an integral and effective hashtag recommendation approach for photo sharing services. In particular, the proposed approach considers both the endogenous and exogenous effects by a content modeling module and a habit modeling module, respectively. For the content modeling module, we adopt the parallel co-attention mechanism to coherently model both image and text as well as the interaction between them; for the habit modeling module, we introduce an external memory unit to characterize the historical tagging habit of each user. The overall hashtag recommendations are generated on the basis of both the post features from the content modeling module and the habit influences from the habit modeling module. We evaluate the proposed approach on real Instagram data. The experimental results demonstrate that the proposed approach significantly outperforms the state-of-theart methods in terms of recommendation accuracy, and that both content modeling and habit modeling contribute significantly to the overall recommendation accuracy.