An Integral Tag Recommendation Model for Textual Content

Authors

  • 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

DOI:

https://doi.org/10.1609/aaai.v33i01.33015109

Abstract

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.

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Published

2019-07-17

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. https://doi.org/10.1609/aaai.v33i01.33015109

Issue

Section

AAAI Technical Track: Machine Learning