Abstract
Recommending academic teams for users of scholars’ social network systems is of great practical value for promoting communication among scholars. This paper proposed a hybrid recommendation model based on time dimension for academic teams. The model combines the three dimensions (the similarity of user and team, good friends and hot teams), and generates a list of team recommendation based on different weights given by the team’s creation time. Experiments on the SCHOLAT data set show that the proposed model can effectively improve the recommendation accuracy and coverage, and solve the cold start problem to a certain extent.
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SCHOLAT Homepage. http://www.scholat.com/
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61772211), the Science and Technology Program of Guangzhou, China (Nos. 201604046017 and 201704020203), the Science and Technology Project of Guangdong Province, China (No. 2016A030303058).
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Tang, Y., Lin, J., Chu, H., He, J., Luo, F. (2019). A Hybrid Recommendation Model Based on Time Dimension for Academic Teams. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_62
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DOI: https://doi.org/10.1007/978-3-030-15127-0_62
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