A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Purpose: Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This study focuses on citation recommendation in the related studies section by applying the term function of a citation context, potentially improving the efficiency of writing a literature review. Design/methodology/approach: We present nine term functions with three newly created and six identified from existing literature. Using these term functions as labels, we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy. BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation. Then the term function information is applied to enhance the performance. Findings: The experiments show that the term function-based methods outperform the baseline methods regarding the recall, precision, and F1-score measurement, demonstrating that term functions are useful in identifying valuable citations. Research limitations: The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section. More recent deep learning models should be performed to future validate the proposed approach. Practical implications: The citation recommendation strategy can be helpful for valuable citation discovery, semantic scientific retrieval, and automatic literature review generation. Originality/value: The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users, improving the transparency, persuasiveness, and effectiveness of recommender systems.