Abstract
With the rapid growth of the scholarly literature, finding relevant and influential articles is becoming increasingly important. Research shows that a scholar’s past works represent his latent interests. However, its effectiveness in recommending scholarly papers has not been well explored in the existing studies. In this paper, we propose an academic paper recommendation model, called CPM, which mainly mines researcher’s published works for improving scholarly navigation. Firstly, a scholar’s papers are divided into different interest points by clustering technologies. Then, scholar’s information needs are represented in terms of pattern equivalence classes. Finally, matching degree and preference degree are integrated to rank the candidate papers. Experimental results on real datasets demonstrate that CPM outperforms (5.6% in terms of NDCG@5 and 8.1% in terms of MRR) the baseline method.
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The work is supported by the National Natural Science Foundation of China (No. 61662053).
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Chen, J., Ban, Z. (2019). Academic Paper Recommendation Based on Clustering and Pattern Matching. In: Knight, K., Zhang, C., Holmes, G., Zhang, ML. (eds) Artificial Intelligence. ICAI 2019. Communications in Computer and Information Science, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-32-9298-7_14
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DOI: https://doi.org/10.1007/978-981-32-9298-7_14
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