Chronological Citation Recommendation with Information-Need Shifting

Abstract

As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called Citation which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content time-varying dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final Citation rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.