We study the problem of author-paper correlation inference in big scholarly data, which is to effectively infer potential correlated works for researchers using historical records. Unlike supervised learning algorithms that predict relevance score of author-paper pair via time and memory consuming feature engineering, network embedding methods automatically learn nodes' representations that can be further used to infer author-paper correlation. However, most current models suffer from two limitations: (1) they produce general purpose embeddings that are independent of the specific task; (2) they are usually based on network structure but out of content semantic awareness. To address these drawbacks, we propose a task-guided and semantic-aware ranking model. First, the historical interactions among all correlated authorpaper pairs are formulated as a pairwise ranking loss. Next, the paper's semantic embedding encoded by gated recurrent neural network, together with the author's latent feature is used to score each author-paper pair in ranking loss. Finally, a heterogeneous relations integrative learning module is designed to further augment the model. The evaluation results of extensive experiments on the well known AMiner dataset demonstrate that the proposed model reaches significant better performance, comparing to a number of baselines.
|Title of host publication
|Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
|International Joint Conferences on Artificial Intelligence
|Number of pages
|Published - 2018
|27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: Jul 13 2018 → Jul 19 2018
|IJCAI International Joint Conference on Artificial Intelligence
|27th International Joint Conference on Artificial Intelligence, IJCAI 2018
|07/13/18 → 07/19/18
Bibliographical noteFunding Information:
We would like to thank Yuxiao Dong for suggestions. This work is supported by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 and the National Science Foundation (NSF) grant IIS-1447795. This work is partially supported by King Abdullah University of Science and Technology (KAUST).
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ASJC Scopus subject areas
- Artificial Intelligence