TY - JOUR AU - Tang, Shijie AU - Yao, Yuan AU - Zhang, Suwei AU - Xu, Feng AU - Gu, Tianxiao AU - Tong, Hanghang AU - Yan, Xiaohui AU - Lu, Jian PY - 2019/07/17 Y2 - 2024/03/28 TI - An Integral Tag Recommendation Model for Textual Content JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v33i01.33015109 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4444 SP - 5109-5116 AB - <p>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) <em>sequential text modeling</em> meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) <em>tag correlation</em> meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) <em>content-tag overlapping</em> 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.</p> ER -