An Attentive Deep Supervision based Semantic Matching Framework For Tag Recommendation in Software Information Sites

Tag recommendation in software information sites is a popular way to help developers classify software objects. Existing methods mostly consider tag recommendation as a multi-label classification task, which does not adequately leverage the semantic information of tags themselves. It is observed that the information granularity of tags is from abstract to specific and deep learning models have proven capable of automatically learning the features in different layers of an integrated network with different abstraction degrees. In this paper, we propose TagMatchRec, a deep semantic matching framework for tag recommendation instead of being based on classification. In our framework, multiple layers with different information granularities are directly connected to the output layer aiming at improving the quality of tag recommendation. Moreover, because the abstraction levels of semantic features learned by each layer may be different given different software objects and tags, an attentive deep supervision is introduced so that the dense connections from early layers to the output layer have directly weighted impact on loss function optimization. Comprehensive evaluations are conducted the datasets from four software information sites. The experimental results show that TagMatchRec has achieved better performance compared with the state-of-the-art approaches.