Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks

Current recommendation works mainly rely on the semantic information of meta-paths sampled from the heterogeneous information network (HIN). However, the diversity of meta-path sampling has not been well guaranteed. Moreover, changes in user's reading preferences and paper's audiences in the short term are often overshadowed by long-term fixed trends. In this paper, we propose a paper recommendation model, called COMRec, where the diversity and dynamics are jointly exploited in HIN. To enhance the semantic diversity of meta-path, we propose a novel in-out degree sampling method that can comprehensively capture the diverse semantic relationships between different types of entities. To incorporate the dynamic changes into the recommended results, we propose a compensation mechanism based on the Bi-directional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) to mine the dynamic trend. Extensive experiments results demonstrate that COMRec outperforms the representative baselines.