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A scientific paper recommender system (RS) is very helpful for literature searching in that it (1) helps novice researchers explore their own field and (2) helps experienced researchers explore new fields outside their area of expertise. However, existing RSs usually recommend relevant papers based on users' static interests, i.e., papers they cited in their past publication(s) or reading histories. In this paper, we propose a novel recommendation task based on users' dynamic interests during their paper-writing activity. This dynamism is revealed in (for example) the topic shift while writing the Introduction vs. Related Works section. In solving this task, we developed a new pipeline called " S ection-based S equential S cientific Pa per R ecommendation (S3PaR)", which recommends papers based on the context of the given user's currently written paper section. Our experiments demonstrate that this unique task and our proposed pipeline outperform existing standard RS baselines. [ABSTRACT FROM AUTHOR]