A Comprehensive Evaluation of Scholarly Paper Recommendation Using Potential Citation Papers
Research Area: Digital Libraries Year: 2015
Type of Publication: Article  
  • Kazunari Sugiyama
  • Min-Yen Kan
To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, in our former work, we identified ``potential citation papers'' through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigated which sections of papers can be leveraged to represent papers effectively. While this initial approach works well for researchers vested in a single discipline, it generates poor predictions for scientists who work on several different topics in the discipline (hereafter, ``intra-disciplinary''). We thus extend our previous work in this paper by proposing an adaptive neighbor selection method to overcome this problem in our imputation-based collaborative filtering framework. On a publicly-available scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when using our adaptive neighbor selection method. While recommendation performance is enhanced for all researchers, improvements are more marked for intra-disciplinary researchers, showing that our method does address the targeted audience.
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