Citation recommendation is an important task to assist scholars in finding candidate literature to cite. Traditional studies focus on static models of recommending citations, which do not explicitly distinguish differences between papers that are caused by temporal variations. Although, some researchers have investigated chronological citation recommendation by adding time related function or modeling textual topics dynamically. These solutions can hardly cope with function generalization or cold-start problems when there is no information for user profiling or there are isolated papers never being cited. With the rise and fall of science paradigms, scientific topics tend to change and evolve over time. People would have the time preference when citing papers, since most of the theoretical basis exist in classical readings that published in old time, while new techniques are proposed in more recent papers. To explore chronological citation recommendation, this paper wants to predict the time preference based on user queries, which is a probability distribution of citing papers published in different time slices. Then, we use this time preference to re-rank the initial citation list obtained by content-based filtering. Experimental results demonstrate that task performance can be further enhanced by time preference and it's flexible to be added in other citation recommendation frameworks.

中文翻译：

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按时间顺序的时间顺序引用建议

引文推荐是一项重要的任务，可以帮助学者们找到可供引用的候选文献。传统研究集中在推荐引文的静态模型上，该模型没有明确地区分时间差异引起的论文差异。虽然，一些研究人员通过添加与时间相关的功能或动态建模文本主题来研究按时间顺序的引用建议。当没有用户配置文件的信息或从未引用过的独立论文时，这些解决方案几乎无法解决功能泛化或冷启动问题。随着科学范式的兴衰，科学主题趋于随着时间而变化和发展。人们在引用论文时会有时间偏好，因为大多数理论基础都存在于旧出版的古典读物中，而新技术则在较新的论文中提出。为了探索按时间顺序的引用建议，本文希望基于用户查询来预测时间偏好，这是在不同时间范围内发表的引用论文的概率分布。然后，我们使用此时间首选项对通过基于内容的过滤获得的初始引用列表进行重新排序。实验结果表明，可以通过时间偏好来进一步提高任务性能，并且可以灵活地将其添加到其他引用建议框架中。这是在不同时间段内发表的引文的概率分布。然后，我们使用此时间首选项对通过基于内容的过滤获得的初始引用列表进行重新排序。实验结果表明，可以通过时间偏好来进一步提高任务性能，并且可以灵活地将其添加到其他引用建议框架中。这是在不同时间段内发表的引文的概率分布。然后，我们使用此时间首选项对通过基于内容的过滤获得的初始引用列表进行重新排序。实验结果表明，可以通过时间偏好来进一步提高任务性能，并且可以灵活地将其添加到其他引用建议框架中。