  Data are essential for the experiments of relevant scientific publication
recommendation methods but it is difficult to build ground truth data. A
naturally promising solution is using publications that are referenced by
researchers to build their ground truth data. Unfortunately, this approach has
not been explored in the literature, so its applicability is still a gap in our
knowledge. In this research, we systematically study this approach by
theoretical and empirical analyses. In general, the results show that this
approach is reasonable and has many advantages. However, the empirical analysis
shows both positive and negative results. We conclude that, in some situations,
this is a useful alternative approach toward overcoming data limitation. Based
on this approach, we build and publish a dataset in computer science domain to
help advancing other researches.
