Scholarly paper recommendation via user's recent research interests 论文

摘要

We examine the effect of modeling a researcher’s past works in recommending scholarly papers to the researcher. Our hypothesis is that an author’s published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works ’ referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous – when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy.