Abstract
The academic social network platform is different from the popular social network platform. Most users of the academic social network platform are researchers and they have more academic related information. How to effectively recommend friend for them according to the characteristics of academic social network have become one of the current research directions. Because of traditional recommendation methods only consider the superficial interaction characteristics, it cannot obtain the more complex and diverse nonlinear relationship between the target user and the recommendation item. The method based on deep learning also has problems such as information loss. In this paper, we propose a friend recommendation model based on multi-dimensional academic characteristics and attention mechanism (MLP–AMAF). We capture the attribute features, relation features and text features of the user from academic social networking. Then we combine these academic features and get the key information related to the current recommendation task automatically by attention mechanism. According to the different preferences of the user, we can get more personalized recommendation results. In our experiments, we use friend data of SCHOLAT, a real academic social network platform, to evaluate our model and get better recommendation results than other widely used recommendation algorithms.
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Acknowledgements
Our works were supported by the National Natural Science Foundation of China (No. U1811263, No. 61772211) and Innovation Team in Guangdong Provincial Department of Education (No. 2018-64/8S0177).
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He, Y., Wang, L., Mao, C., Li, Y., Sun, S., Cai, Y. (2019). Friend Recommendation Model Based on Multi-dimensional Academic Feature and Attention Mechanism. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_37
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DOI: https://doi.org/10.1007/978-981-15-1377-0_37
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