  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.
