Semantic Tag Recommendation Using Concept Model

The common tags given by multiple users to a particular document are often semantically relevant to the document and each tag represents a specific topic. In this paper, we attempt to emulate human tagging behavior to recommend tags by considering the concepts contained in documents. Specifically, we represent each document using a few most relevant concepts contained in the document, where the concept space is derived from Wikipedia. Tags are then recommended based on the tag concept model derived from the annotated documents of each tag. Evaluated on a Delicious dataset of more than 53K documents, the proposed technique achieved comparable tag recommendation accuracy as the state-of-the-art, while yielding an order of magnitude speed-up.