Abstract
With the increasing number of microblog users, the hashtag recommendation task has become an important component in social media. Most hashtag recommendation related methods get relative low precisions, because hashtags are not necessarily related to the content of tweets, which makes hashtag recommendation more challenging. In this work, we propose a new sequence-to-sequence method named attention based neural image hashtagging network (A-NIH) to model sequence relationship between social images and hashtags. To the best of our knowledge, this is the first work that applies attention mechanism to the image-only hashtag recommendation tasks. Our experimental results on the real-world social image dataset shows that our model performs better than the state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Berendt, B., Hanser, C.: Tags are not metadata, but “just more content” - to some people. In: Proceedings of the First International Conference on Weblogs and Social Media, ICWSM 2007, Boulder, Colorado, USA, 26–28 March 2007 (2007)
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
Dey, K., Shrivastava, R., Kaushik, S., Subramaniam, L.V.: Emtagger: a word embedding based novel method for hashtag recommendation on twitter. In: 2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, 18–21 November 2017, pp. 1025–1032 (2017)
Efron, M.: Hashtag retrieval in a microblogging environment. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, 19–23 July 2010, pp. 787–788 (2010)
Gong, Y., Zhang, Q., Huang, X.: Hashtag recommendation for multimodal microblog posts. Neurocomputing 272, 170–177 (2018)
Gong, Y., Zhang, Q.: Hashtag recommendation using attention-based convolutional neural network. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2782–2788 (2016)
Huang, H., Zhang, Q., Gong, Y., Huang, X.: Hashtag recommendation using end-to-end memory networks with hierarchical attention. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 11–16 December 2016, Osaka, Japan, pp. 943–952 (2016)
Jocic, M., Obradovic, D., Malbasa, V., Konjo, Z.: Image tagging with an ensemble of deep convolutional neural networks. In: 2017 International Conference on Information Society and Technology, ICIST Workshops 2017, New Orleans, LA, USA, 18–21 November 2017, pp. 13–17 (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014)
Li, Y., Liu, T., Jiang, J., Zhang, L.: Hashtag recommendation with topical attention-based LSTM. In: COLING 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 11–16 December 2016, Osaka, Japan, pp. 3019–3029 (2016)
Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 8–13 December 2014, pp. 2204–2212 (2014)
Park, M., Li, H., Kim, J.: HARRISON: A benchmark on hashtag recommendation for real-world images in social networks. CoRR abs/1605.05054 (2016)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Sedhai, S., Sun, A.: Hashtag recommendation for hyperlinked tweets. In: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, Gold Coast, QLD, Australia, 06–11 July 2014, pp. 831–834 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 2818–2826 (2016)
Wang, L., Guo, S., Huang, W., Qiao, Y.: Places205-vggnet models for scene recognition. CoRR abs/1508.01667 (2015)
Wang, Y.: Image tag recommendation algorithm using tensor factorization. J. Multimed. 9(3), 416–422 (2014)
Xu, K., et al.: Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015, pp. 2048–2057 (2015)
Zhang, Q., Wang, J., Huang, H., Huang, X., Gong, Y.: Hashtag recommendation for multimodal microblog using co-attention network. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19–25 August 2017, pp. 3420–3426 (2017)
Zhou, B., Lapedriza, À., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, 8–13 December 2014, pp. 487–495 (2014)
Acknowledgments
The authors wish to thank the anonymous reviewers for their helpful comments, and we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research, what’s more, we feel thankful that authors can provide baseline models for us so that we can conduct the experiments smoothly. This work is supported by the National Key Research and Development Program of China under grants 2016QY01W0202 and 2016YFB0800402, National Natural Science Foundation of China under grants 61572221, U1401258, 61433006, 61772219 and 61502185, Major Projects of the National Social Science Foundation under grant 16ZDA092, Science and Technology Support Program of Hubei Province under grant 2015AAA013, Science and Technology Program of Guangdong Province under grant 2014B010111007 and Guangxi High level innovation Team in Higher Education Institutions–Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, G., Li, Y., Yan, W., Li, R., Gu, X., Yang, Q. (2018). Hashtag Recommendation with Attention-Based Neural Image Hashtagging Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-04179-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04178-6
Online ISBN: 978-3-030-04179-3
eBook Packages: Computer ScienceComputer Science (R0)