  Graph Convolutional Networks have made significant strides in Collabora-tive
Filtering recommendations. However, existing GCN-based CF methods are mainly
based on matrix factorization and incorporate some optimization tech-niques to
enhance performance, which are not enough to handle the complexities of diverse
real-world recommendation scenarios. E-government service recommendation is a
crucial area for recommendation re-search as it involves rigid aspects of
people's lives. However, it has not received ad-equate attention in comparison
to other recommendation scenarios like news and music recommendation. We
empirically find that when existing GCN-based CF methods are directly applied
to e-government service recommendation, they are limited by the MF framework
and showing poor performance. This is because MF's equal treatment of users and
items is not appropriate for scenarios where the number of users and items is
unbalanced. In this work, we propose a new model, GCNSLIM, which combines GCN
and sparse linear methods instead of combining GCN and MF to accommodate
e-government service recommendation. In particular, GCNSLIM explicitly injects
high-order collaborative signals obtained from multi-layer light graph
convolutions into the item similarity matrix in the SLIM frame-work,
effectively improving the recommendation accuracy. In addition, we propose two
optimization measures, removing layer 0 embedding and adding nonlinear
acti-vation, to further adapt to the characteristics of e-government service
recommenda-tion scenarios. Furthermore, we propose a joint optimization mode to
adapt to more diverse recommendation scenarios. We conduct extensive
experiments on a real e-government service dataset and a common public dataset
and demonstrate the ef-fectiveness of GCNSLIM in recommendation accuracy and
operational performance.
