An Integral Tag Recommendation Model for Textual Content


  • Shijie Tang Nanjing University
  • Yuan Yao Nanjing University
  • Suwei Zhang Nanjing University
  • Feng Xu Nanjing University
  • Tianxiao Gu University of California, Davis
  • Hanghang Tong Arizona State University
  • Xiaohui Yan Huawei Technologies
  • Jian Lu Nanjing University



Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.




How to Cite

Tang, S., Yao, Y., Zhang, S., Xu, F., Gu, T., Tong, H., Yan, X., & Lu, J. (2019). An Integral Tag Recommendation Model for Textual Content. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 5109-5116.



AAAI Technical Track: Machine Learning