Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning Approach

Abstract

The tagging system has become a primary tool to organize information resources on the Internet, which benefits both users and the platforms. To build a successful tagging system, automatic tagging methods are desired. With the development of society, new tags keep emerging. The problem of tagging items with emerging tags is an open challenge for an automatic tagging system, and it has not been well studied in the literature. We define this problem as a tag-centered cold-start problem in this study and propose a novel neural topic model based few-shot learning method named NTFSL to solve the problem. In our proposed method, we innovatively fuse the topic modeling task with the few-shot learning task, endowing the model with the capability to infer effective topics to solve the tag-centered cold-start problem with the property of interpretability. Meanwhile, we propose a novel neural topic model for the topic modeling task to improve the quality of inferred topics, which helps enhance the tagging performance. Furthermore, we develop a novel inference method based on the variational auto-encoding framework for model inference. We conducted extensive experiments on two real-world datasets, and the results demonstrate the superior performance of our proposed model compared with state-of-the-art machine learning methods. Case studies also show the interpretability of the model.