Capturing and Exploiting Citation Knowledge for Recommending Recently Published Papers

Abstract

With the continuous growth of scientific literature, discovering relevant academic papers for a researcher has become a challenging task, especially when looking for the latest, most recent papers. In this case, traditional collaborative filtering systems are ineffective, since they are unable to recommend items not previously seen, rated or cited. In this paper, we explore the potential of exploiting citation knowledge to provide a given user with relevant suggestions about recent scientific publications. A novel hybrid recommendation method that encapsulates such citation knowledge is proposed. Experimental results show improvements over baseline methods, evidencing benefits of using citation knowledge to recommend recently published papers in a personalised way. Moreover, as a result of our work, we also provide a unique dataset that, differently to previous corpora, contains detailed paper citation information.

Paper Recommendation Using SPECTER with Low-Rank and Sparse Matrix Factorization

“…The author's profile has been strengthened through publications that have cited the author's work. Khadka et al [28] used their thematic information to generate a high-level representation of a paper to generate citation suggestions. However, the CB model can be plagued by cold start and over-specialization issues [29].…”

Towards Generating Citation Sentences for Multiple References with Intent Control

“…Scientific facts should be presented accurately, methods to examine the correctness of the generated results should therefore be included. Moreover, there are increasing research results regarding to citation recommendation (Nogueira et al, 2020;Giosa and Di Caro, 2020;Ali et al, 2020;Khadka, 2020;Ali et al, 2021). Pipelines combining citation recommendation and generation can make the workflow of reviewing scientific literature more efficient.…”