  The sequential recommendation system has been widely studied for its
promising effectiveness in capturing dynamic preferences buried in users'
sequential behaviors. Despite the considerable achievements, existing methods
usually focus on intra-sequence modeling while overlooking exploiting global
collaborative information by inter-sequence modeling, resulting in inferior
recommendation performance. Therefore, previous works attempt to tackle this
problem with a global collaborative item graph constructed by pre-defined
rules. However, these methods neglect two crucial properties when capturing
global collaborative information, i.e., adaptiveness and personalization,
yielding sub-optimal user representations. To this end, we propose a
graph-driven framework, named Adaptive and Personalized Graph Learning for
Sequential Recommendation (APGL4SR), that incorporates adaptive and
personalized global collaborative information into sequential recommendation
systems. Specifically, we first learn an adaptive global graph among all items
and capture global collaborative information with it in a self-supervised
fashion, whose computational burden can be further alleviated by the proposed
SVD-based accelerator. Furthermore, based on the graph, we propose to extract
and utilize personalized item correlations in the form of relative positional
encoding, which is a highly compatible manner of personalizing the utilization
of global collaborative information. Finally, the entire framework is optimized
in a multi-task learning paradigm, thus each part of APGL4SR can be mutually
reinforced. As a generic framework, APGL4SR can outperform other baselines with
significant margins. The code is available at
https://github.com/Graph-Team/APGL4SR.
