Abstract
The accuracy of information network partition is not high and the characteristics of metapath cannot represent the attributes of network nodes in the existing academic citation recommendation algorithms. In order to solve the problems, a similarity measurement algorithm, community merging and time effect PathSim (CMTE-PathSim), based on community merging and time effect is proposed. On the premise of dividing heterogeneous information network (HIN) effectively, the algorithm considers the influence of node information on the characteristics of metapath. The results of Top-k query verify the effectiveness of CMTE-PathSim on real datasets and improve the quality of citation recommendation.
Supported by the Scientific Research Fund of Liaoning Provincial Education Department (L2019048), and Talent Scientific Research Rund of LSHU (2016XJJ-033) of China.
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Xing, L., Jin, L., Jia, Y., Wu, C. (2021). Citation Recommendation Based on Community Merging and Time Effect. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_6
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