Semantic-based tag recommendation in scientific bookmarking systems.

Recently, tagging has become a common way for users to organize and share digital content, and tag recommendation (TR) has become a very important research topic. Most of the recommendation approaches which are based on text embedding have utilized bag-of-words technique. On the other hand, proposed deep learning methods for capturing semantic meanings in the text, have been proved to be effective in various natural language processing (NLP) applications. In this paper, we present a content-based TR method that adopts deep recurrent neural networks to encode titles and abstracts of scientific articles into semantic vectors for enhancing the recommendation task, specifically bidirectional gated recurrent units (bi-GRUs) with attention mechanism. The experimental evaluation is performed on a dataset from CiteULike. The overall findings show that the proposed model is effective in representing scientific articles for tag recommendation.