Chronological Citation Recommendation with Time Preference
Shutian Ma, Heng Zhang, Chengzhi Zhang, Xiaozhong Liu

TL;DR
This paper proposes a method for improving citation recommendation by modeling and incorporating users' time preferences, which reflect the temporal citing behavior, to better rank relevant papers.
Contribution
It introduces a novel approach to predict and utilize time preferences in citation recommendation, addressing limitations of static and existing chronological models.
Findings
Time preference modeling improves citation ranking accuracy.
The method enhances existing content-based filtering frameworks.
Experimental results show significant performance gains.
Abstract
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…
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