Abstract
In this paper we address the problem of providing personalised recommendations of recent scientific publications to a particular user, and explore the use of citation knowledge to do so. For this purpose, we have generated a novel dataset that captures authors’ publication history and is enriched with different forms of paper citation knowledge, namely citation graphs, citation positions, citation contexts, and citation types. Through a number of empirical experiments on such dataset, we show that the exploitation of the extracted knowledge, particularly the type of citation, is a promising approach for recommending recently published papers that may not be cited yet. The dataset, which we make publicly available, also represents a valuable resource for further investigation on academic information retrieval and filtering.- Anthology ID:
- 2020.lrec-1.272
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2231–2240
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.272
- DOI:
- Cite (ACL):
- Anita Khadka, Iván Cantador, and Miriam Fernandez. 2020. Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2231–2240, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Exploiting Citation Knowledge in Personalised Recommendation of Recent Scientific Publications (Khadka et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.272.pdf