Textual tag recommendation with multi-tag topical attention

Tagging can be regarded as the action of connecting relevant user-defined keywords to an item, indirectly improving the quality of the information retrieval services that rely on tags as data sources. Tag recom-mendation dramatically enhances the quality of tags by assisting users in tagging. Although there exist many studies on tag recommendation for textual content, few of them consider two characteristics in real applications, i.e., the long-tail distribution of tags and the topic-tag correlation. In this paper, we propose a Topic-Guided Tag Recommendation (TGTR) model to recommend tags by jointly incorporating dynamic neural topic. Specifically, TGTR first generates dynamic neural topic that would indicate the tags by a neural topic generator. Then, a sequence encoder is used to distill indicative features from the post. To effectively leverage the topic and alleviate the data imbalance, we design a multi-tag topical attention mechanism to get a tag-specific post representation for each tag with the help of dynamic neural topic. These three modules are seamlessly joined together via an end-to-end multi-task learning model, which is helpful for the three parts to enhance each other and balance the effects of topics and tags. Extensive experiments have been conducted on four real-world datasets and demonstrate that our model outper-forms the state-of-the-art approaches by a large margin, especially on tail-tags. The code, data and hyper -parameter settings are publicly released for reproducibility. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Tag recommendation improves the quality of information retrieval services by assisting users in tagging. However, existing studies rarely consider the long-tail distribution of tags and the topic-tag correlation. This paper proposes a Topic-Guided Tag Recommendation (TGTR) model that incorporates dynamic neural topics to recommend tags and balances the effects of topics and tags. Experimental results show that our model outperforms state-of-the-art approaches, especially on tail-tags.