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| Scholar Recommendation Research Based on Academic Keywords and Co-citation |
| Xiong Huixiang, Li Xiaomin, Du Jin |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract The surge in academic data has caused an information overload, which, in turn, burdens scientific research users. Research scholars recommend a method that can improve the efficiency of scientific research and facilitate the smooth development of scientific research. This paper constructs a personalized chemist recommendation model based on combined similarity calculation. The combined similarity calculation includes calculations based on the similarity of scholar feature words and calculations based on the co-citation similarity of scholars. The former considers the similarity of scholars based on the research content, while the latter considers the similarity of scholars based on the co-citation relationship. At the same time, the data in the Chinese Social Sciences Citation Index (CSSCI) database and China National Knowledge Infrastructure (CNKI) are used for model verification, and the accuracy rate, recall rate, and F value are used to evaluate the recommendation effect. The experimental results show that the recommendation model proposed in this paper has achieved good results; thus, it can be recommended for target scholars. Scholars with similar research interests promote academic communication.
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Received: 04 June 2020
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