%0 Conference Paper %1 F_rber_2020 %A Färber, Michael %A Sampath, Ashwath %B Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 %D 2020 %I ACM %K academic-reference jcdl2020 recommender %P 117-126 %R 10.1145/3383583.3398534 %T HybridCite: A Hybrid Model for Context-Aware Citation Recommendation %U https://doi.org/10.1145%2F3383583.3398534 %X Citation recommendation systems aim to recommend citations for either a complete paper or a small portion of text called a citation context. The process of recommending citations for citation contexts is called local citation recommendation and is the focus of this paper. Firstly, we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques. We combine, for the first time to the best of our knowledge, the best-performing algorithms into a semi-genetic hybrid recommender system for citation recommendation. We evaluate the single approaches and the hybrid approach offline based on several data sets, such as the Microsoft Academic Graph (MAG) and the MAG in combination with arXiv and ACL. We further conduct a user study for evaluating our approaches online. Our evaluation results show that a hybrid model containing embedding and information retrieval-based components outperforms its individual components and further algorithms by a large margin.

@inproceedings{F_rber_2020, abstract = {Citation recommendation systems aim to recommend citations for either a complete paper or a small portion of text called a citation context. The process of recommending citations for citation contexts is called local citation recommendation and is the focus of this paper. Firstly, we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques. We combine, for the first time to the best of our knowledge, the best-performing algorithms into a semi-genetic hybrid recommender system for citation recommendation. We evaluate the single approaches and the hybrid approach offline based on several data sets, such as the Microsoft Academic Graph (MAG) and the MAG in combination with arXiv and ACL. We further conduct a user study for evaluating our approaches online. Our evaluation results show that a hybrid model containing embedding and information retrieval-based components outperforms its individual components and further algorithms by a large margin. }, added-at = {2020-11-23T03:41:16.000+0100}, author = {Färber, Michael and Sampath, Ashwath}, biburl = {https://www.bibsonomy.org/bibtex/2a778c52480f26ff22c827eb7eb196740/brusilovsky}, booktitle = {Proceedings of the {ACM}/{IEEE} Joint Conference on Digital Libraries in 2020}, description = {HybridCite: A Hybrid Model for Context-Aware Citation Recommendation | Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020}, doi = {10.1145/3383583.3398534}, interhash = {c775ac687553bc9f469bf86ab97ae830}, intrahash = {a778c52480f26ff22c827eb7eb196740}, keywords = {academic-reference jcdl2020 recommender}, month = aug, pages = {117-126}, publisher = {{ACM}}, timestamp = {2020-11-23T03:41:16.000+0100}, title = {{HybridCite}: A Hybrid Model for Context-Aware Citation Recommendation}, url = {https://doi.org/10.1145%2F3383583.3398534}, year = 2020 }