Due to the short attention span and instant gratification phenomenon, micro-videos are growing exponentially while gaining more and more concerns. Yet the sheer number of micro-videos leads to severe information overload issues, making it difficult for users to identify their desired micro-videos. The hashtag, mainly utilized in the domain of the microblog or the image, is the indicator or the core idea of the target content and can be applied to various information retrieval scenarios (e.g., search, browse, and categorization). So far, however, little attention has been paid to perform the hashtag recommendation for micro-videos via harnessing multiple modalities.

In this article, we devise a neural network-based solution, LOGO (short for “muLti-mOdal-based hashtaG recOmmendation”), to recommend hashtags for micro-videos by utilizing multiple modalities. The proposed LOGO approach first represents each modality as the combination of sequential units in it, weighted by the attention mechanism. In this way, the sequential and attentive features are captured simultaneously. After that, the LOGO integrates the representations of all modalities via a multi-view representation learning framework, which projects the representations into a common space under the restriction of the modality similarity. Ultimately, the LOGO feed the projections of three modalities in the common space and the embeddings of hashtags into a customized neural collaborative filtering framework to perform the hashtag recommendation. Extensive experiments on the scope of both overall performance comparison and micro-level analyses have well-justified the effectiveness and rationality of our proposed approach.

由于关注时间短和即时满足的现象，微型视频正成倍增长，同时引起了越来越多的关注。但是，微型视频的数量过多会导致严重的信息过载问题，从而使用户难以识别所需的微型视频。主题标签主要用于微博或图像领域，是目标内容的指示符或核心思想，可以应用于各种信息检索方案（例如，搜索，浏览和分类）。但是，到目前为止，很少有人关注通过利用多种模式来执行微视频的主题标签推荐。

在本文中，我们设计一个基于神经网络的溶液，LOGO（简称“亩大号TI-米ö基于DAL-hashta ģ REC ömmendation”），以通过利用多种方式为微型视频推荐标签。所提出的LOGO方法首先将每个模式表示为其中的顺序单元的组合，并通过注意力机制对其进行加权。这样，可以同时捕获顺序和注意特征。之后，LOGO会通过多视图表示学习框架集成所有模态的表示，该框架在模态相似性的限制下将表示投影到公共空间中。最终，LOGO将公共空间中三种模态的投影以及主题标签的嵌入反馈到定制的神经协作过滤框架中，以执行主题标签推荐。