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| Research on Interdisciplinary Paper Recommendation Based on Academic Network |
| Du Jin, Xiong Huixiang, Xiang Yinghong |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract To meet the needs of information science researchers for interdisciplinary papers, this study develops an interdisciplinary paper recommendation model based on academic network. First, according to the keyword coupling network and author citation network characteristics, the correlation between the author and the paper is extracted, facilitating paper recommendation based on keyword coupling. Second, this model utilizes the author citation coupling network, encompassing cross-disciplinary citation relationships, co-citation relationships, and the subject attribute of the paper. This information is used to mine interdisciplinarity aspects of authors and papers, calculating interdisciplinary similarity for subject-based recommendations. Finally, the study integrates recommendations based on keyword coupling and subject similarity to achieve a hybrid recommendation for interdisciplinary papers. The model is validated using data from Chinese Social Sciences Citation Index (CSSCI) database. Empirical results demonstrate that the recommended papers exhibit interdisciplinary characteristics. Compared with keyword coupling-based recommendations, combining interdisciplinary characteristics improves author recommendation success rates, average accuracy rates, and average recall rates.
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Received: 22 May 2023
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