Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling
| dc.contributor.author | Ji, Taoran | en |
| dc.contributor.author | Self, Nathan | en |
| dc.contributor.author | Fu, Kaiqun | en |
| dc.contributor.author | Chen, Zhiqian | en |
| dc.contributor.author | Ramakrishnan, Naren | en |
| dc.contributor.author | Lu, Chang-Tien | en |
| dc.date.accessioned | 2024-03-01T13:17:26Z | en |
| dc.date.available | 2024-03-01T13:17:26Z | en |
| dc.date.issued | 2024 | en |
| dc.date.updated | 2024-03-01T08:49:03Z | en |
| dc.description.abstract | Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. | en |
| dc.description.version | Accepted version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1145/3649140 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/118220 | en |
| dc.language.iso | en | en |
| dc.publisher | ACM | en |
| dc.rights | In Copyright | en |
| dc.rights.holder | The author(s) | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.title | Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |