AMNN: Attention-Based Multimodal Neural Network Model for Hashtag Recommendation
| Autor: | Ruixuan Li, Qi Yang, Yuhua Li, Huicai Deng, Gaosheng Wu, Junzhuang Wu, Xiwu Gu |
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| Rok vydání: | 2020 |
| Předmět: |
Source code
Information retrieval Artificial neural network Computer science Microblogging media_common.quotation_subject Feature extraction 02 engineering and technology Data modeling Task (project management) Human-Computer Interaction Hybrid neural network 020204 information systems Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Social media Social Sciences (miscellaneous) media_common |
| Zdroj: | IEEE Transactions on Computational Social Systems. 7:768-779 |
| ISSN: | 2373-7476 |
| DOI: | 10.1109/tcss.2020.2986778 |
| Popis: | In the real-world social networks, hashtags are widely applied for understanding the content of an individual microblog. However, users do not always take the initiative in attaching hashtags when posting a microblog so that much effort has been invested for automatically hashtag recommendation. As a new trend, users no longer only post texts but prefer to share with multimodal data, such as images. To deal with these situations, we propose an attention-based multimodal neural network model (AMNN) to learn the representations of multimodal microblogs and recommend relevant hashtags. In this article, we convert the hashtag recommendation task into a sequence generation problem. Then, we propose a hybrid neural network approach to extract the features of both texts and images and incorporate them into the sequence-to-sequence model for hashtag recommendation. Experimental results on the data set collected on Instagram and two public data sets demonstrate that the proposed method outperforms state-of-the-art methods. Our model achieves the best performance in three different metrics: precision, recall, and accuracy. The source code of this article can be obtained from “ https://github.com/w5688414/AMNN .” |
| Databáze: | OpenAIRE |
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