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.

翻译：序列推荐系统因在捕捉用户序列行为中隐藏的动态偏好方面具有显著有效性而被广泛研究。尽管取得了可观成果，现有方法通常聚焦于序列内建模，而忽视了通过序列间建模利用全局协作信息，导致推荐性能欠佳。为此，先前工作尝试通过预定义规则构建全局协作物品图来解决该问题。然而，这些方法在捕捉全局协作信息时忽略了两项关键属性，即自适应性与个性化，从而产生了次优的用户表示。针对这一问题，我们提出了一种名为"自适应与个性化图学习序列推荐框架（APGL4SR）"的图驱动框架，将自适应与个性化的全局协作信息融入序列推荐系统。具体而言，我们首先在所有物品之间学习自适应全局图，并以自监督方式利用该图捕捉全局协作信息，其计算负担可通过所提出的基于SVD的加速器进一步缓解。此外，基于该图，我们提出以相对位置编码的形式提取并利用个性化物品关联，这是一种高度兼容的个性化利用全局协作信息的方式。最终，整个框架在多任务学习范式下进行优化，从而使APGL4SR的各个部分能够相互增强。作为一个通用框架，APGL4SR能够以显著优势超越其他基线方法。代码开源地址为：https://github.com/Graph-Team/APGL4SR。