Abstract
With the rapid development of online social networks, academic social networks (ASNs) platforms are increasingly favored by researchers. For the scholar services built on the ASNs, recommending personalized researchers have become more important, as it could promote academic communication and scientific research for scholars. We propose a personalized recommendation method combining similarity and trust degree in an academic social network. First, the text-similarity hybrid model of LDA and TF-IDF is used to calculate the similarity of scholars’ interests, moreover, the social similarity between scholars is combined as the final similarity. Second, the trust degree is calculated according to the multi-dimensional interactive behavior among scholars. Finally, the combined similarity and trust degree between scholars are used as a ranking metric. We demonstrate and evaluate our approach with a real dataset from an academic social site SCHOLAT. The experiment results show that our method is valid in recommending personalized researches.
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This work was supported by National Natural Science Foundation of China under grant number U1811263, by National Natural Science Foundation of China under grant number 6177221.
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Qiu, L., Yuan, C., Li, J., Lian, S., Tang, Y. (2021). Personalized Recommendation Based on Scholars’ Similarity and Trust Degree. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_32
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DOI: https://doi.org/10.1007/978-981-16-2540-4_32
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