Abstract
Digital libraries suffer from the overload problem, which makes the researchers have to spend much time to find relevant papers. Fortunately, recommender system can help to find some relevant papers for researchers automatically according to their browsed papers. Previous paper recommendation methods are either citation-based or content-based. In this paper, we propose a novel recommendation method with a heterogeneous graph in which both citation and content knowledge are included. In detail, a heterogeneous graph is constructed to represent both citation and content information within papers. Then, we apply a graph-based similarity learning algorithm to perform our paper recommendation task. Finally, we evaluate our proposed approach on the ACL Anthology Network data set and conduct an extensive comparison with other recommender approaches. The experimental results demonstrate that our approach outperforms traditional methods.
This work was supported by the National Natural Science Foundation of China (No. 61472183, 61333014).
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Pan, L., Dai, X., Huang, S., Chen, J. (2015). Academic Paper Recommendation Based on Heterogeneous Graph. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_31
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DOI: https://doi.org/10.1007/978-3-319-25816-4_31
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