Abstract
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.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61972272, 62172291, 62072321, and U1905211, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 21KJA520008, and the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant No. SJCX21_1344.
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Wang, J., Zhou, J., Wu, Z., Sun, X. (2022). Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_19
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DOI: https://doi.org/10.1007/978-3-031-00126-0_19
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