AutoSR: Automatic Sequential Recommendation System Design

S
equential
R
ecommendation (SR) System emerged recently as a powerful tool for suggesting users with the next item of interest. Despite their great success, the design of SR systems requires heavy manual work and domain knowledge. In this paper, we present
$\mathbf {AutoSR}$
, an effective
Auto
mated
M
achine
L
earning (AutoML) tool that enables automatic design of powerful SR systems based on
G
raph
N
eural
N
etwork (GNN) and
R
einforcement
L
earning (RL). In
$\mathbf {AutoSR}$
, we summarize the design process of the SR system and extract effective operations from the existing SR systems to construct our search space. Such experience-based search space generates diverse SR systems by integrating effective operations of different systems, providing a basic condition for the implementation of AutoML. Besides, we propose a graph-based RL method to efficiently explore the SR search space, where operations have complex and diverse application conditions. Compared with the existing AutoML methods, which ignore potential relations among operations,
$\mathbf {AutoSR}$
can greatly avoid invalid SR system design and efficiently discover more powerful SR systems by analyzing the relation graph of various operations. Extensive experimental results show that
$\mathbf {AutoSR}$
can gain powerful SR systems, superior to the existing
$\mathbf {AutoSR}$
systems used for search space construction. Besides,
$\mathbf {AutoSR}$
is more efficient than the existing AutoML algorithms in SR system design, which demonstrate the superiority of
$\mathbf {AutoSR}$
.