Abstract
With the number of scientific papers growing exponentially, recommending relevant papers for researchers has become an important and attractive research area. Existing paper recommendation methods pay more attention to the textual similarity or the citation relationships between papers. However, they generally ignore the researcher’s dynamic research interests which affect the recommendation performance to a large extent. Additionally, cold start is also a serious problem in existing paper recommender systems since many researchers may have few publications, which makes the recommender systems fail to learn their preferences. In order to solve these issues, in this paper, we propose a Time-Aware Paper Recommendation (TAPRec) model, which learns researchers’ dynamic preferences by encoding the long-term and short-term research interests from their historical publications. The Self-Attention method is utilized to aggregate researchers’ consistent long-term research interests, while the short-term research focuses are implemented with Temporal Convolutional Networks (TCN). In addition, for researchers with few academic achievements, we combine their co-authors’ dynamic preferences to solve the cold-start problem. Experiments with the DBLP dataset indicate that the proposed time-aware model performs better in the recommendation accuracy compared to the state-of-the-arts methods.
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Acknowledgements
This research is funded by the National Natural Science Foundation of China under Grant No.61802440 and No.62102159. The Humanities and Social Science Fund of Ministry of Education of China (Grant No. 21YJC870002) and Knowledge Innovation Program of Wuhan-Shuguang Project (Grant No. 2022010801020287). It is also appreciated that anonymous reviewers make valuable suggestions which have helped improve our manuscript by leaps and bounds. We declare that we have no conflict of interest.
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Jiang, C., Ma, X., Zeng, J. et al. TAPRec: time-aware paper recommendation via the modeling of researchers’ dynamic preferences. Scientometrics 128, 3453–3471 (2023). https://doi.org/10.1007/s11192-023-04731-4
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DOI: https://doi.org/10.1007/s11192-023-04731-4