Abstract
With the development of recommendation algorithms, recommendation systems are being widely used for recommendation tasks in different domains. However, most recommendation systems do not make good use of valid information, resulting in unsatisfactory recommendations. At the same time, the overly complex system design leads to slow recommendation speed, which does not meet the speed requirement. To address the above issues, we introduce a new user recommendation system based on academic social website SCHOLAT, including the key modules of the system and the key processes of the system. Then we introduce the solutions proposed to solve the system performance problem and cold start problem. Finally we introduce the social network dataset of SCHOLAT used for training model, and verify the effect of Variational Graph Normalized Auto-Encoders(VGNAE) on SCHOLAT dataset. We compare the effect of old version and new version recommendation system. The experimental results show that the new version of the recommendation system outperform the old one in terms of recommendation accuracy and relevance. And the system performance also meet the practical application requirements.
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This work was supported in part by the National Natural Science Foundation of China under Grant U1811263 and Grant 62077045.
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Weng, Y., Yu, W., Lin, R., Tang, Y., He, C. (2023). ScholarRec: A User Recommendation System for Academic Social Network. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_3
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DOI: https://doi.org/10.1007/978-981-99-2356-4_3
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