  Searching for papers from different academic databases is the most commonly
used method by research beginners to obtain cross-domain technical solutions.
However, it is usually inefficient and sometimes even useless because
traditional search methods neither consider knowledge heterogeneity in
different domains nor build the bottom layer of search, including but not
limited to the characteristic description text of target solutions and
solutions to be excluded. To alleviate this problem, a novel paper
recommendation method is proposed herein by introducing "master-slave" domain
knowledge graphs, which not only help users express their requirements more
accurately but also helps the recommendation system better express knowledge.
Specifically, it is not restricted by the cold start problem and is a
challenge-oriented method. To identify the rationality and usefulness of the
proposed method, we selected two cross-domains and three different academic
databases for verification. The experimental results demonstrate the
feasibility of obtaining new technical papers in the cross-domain scenario by
research beginners using the proposed method. Further, a new research paradigm
for research beginners in the early stages is proposed herein.
