Towards Unified Modeling for Positive and Negative Preferences in
  Sign-Aware Recommendation
Yuting Liu Yizhou Dang Yuliang Liang Qiang Liu Guibing Guo Jianzhe Zhao Xingwei Wang
Recently, sign-aware graph recommendation has drawn much attention as it will
learn users' negative preferences besides positive ones from both positive and
negative interactions (i.e., links in a graph) with items. To accommodate the
different semantics of negative and positive links, existing works utilize two
independent encoders to model users' positive and negative preferences,
respectively. However, these approaches cannot learn the negative preferences
from high-order heterogeneous interactions between users and items formed by
multiple links with different signs, resulting in inaccurate and incomplete
negative user preferences. To cope with these intractable issues, we propose a
novel \textbf{L}ight \textbf{S}igned \textbf{G}raph Convolution Network
specifically for \textbf{Rec}ommendation (\textbf{LSGRec}), which adopts a
unified modeling approach to simultaneously model high-order users' positive
and negative preferences on a signed user-item interaction graph. Specifically,
for the negative preferences within high-order heterogeneous interactions,
first-order negative preferences are captured by the negative links, while
high-order negative preferences are propagated along positive edges. Then,
recommendation results are generated based on positive preferences and
optimized with negative ones. Finally, we train representations of users and
items through different auxiliary tasks. Extensive experiments on three
real-world datasets demonstrate that our method outperforms existing baselines
regarding performance and computational efficiency. Our code is available at
\url{https://anonymous.4open.science/r/LSGRec-BB95}.