Finding online scientific articles relevant to one’s interests is very challenging due to the increasing number of publications. Hence, scientific paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-of-words techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on tags. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.

Mohamed, H.A.I., Sansonetti, G., Micarelli, A. (2020). Tag-Aware Matrix Factorization for Research Paper Recommendation. In Proceedings of the SIGIR 2020 Workshop on Deep Natural Language Processing for Search and Recommendation.

Tag-Aware Matrix Factorization for Research Paper Recommendation

MOHAMED HEBATALLAH ATEF IBRRAHIM
;
Giuseppe Sansonetti;Alessandro Micarelli
2020

Abstract

Finding online scientific articles relevant to one’s interests is very challenging due to the increasing number of publications. Hence, scientific paper recommendation has become a significant and timely research topic. Collaborative filtering is a successful recommendation approach, which exploits the ratings given to items by users as a source of information for learning to make accurate recommendations. However, the ratings are often very sparse as in the research paper domain, due to the huge number of publications growing every year. Therefore, more attention has been drawn to hybrid methods that consider both ratings and content information. Nevertheless, most of the hybrid recommendation approaches that are based on text embedding have utilized bag-of-words techniques, which ignore word order and semantic meaning. In this paper, we propose a hybrid approach that leverages deep semantic representation of research papers based on tags. The experimental evaluation is performed on CiteULike, a real and publicly available dataset. The obtained findings show that the proposed model is effective in recommending research papers even when the rating data is very sparse.
Mohamed, H.A.I., Sansonetti, G., Micarelli, A. (2020). Tag-Aware Matrix Factorization for Research Paper Recommendation. In Proceedings of the SIGIR 2020 Workshop on Deep Natural Language Processing for Search and Recommendation.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/394114
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact