Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of items, overlooking the collaborative relationships among them. Graph-based methods incorporate collaborative information by utilizing the user-item interaction graph. However, these methods sometimes face challenges in terms of time complexity and computational efficiency. To address these limitations, this paper presents AutoSeqRec, an incremental recommendation model specifically designed for sequential recommendation tasks. AutoSeqRec is based on autoencoders and consists of an encoder and three decoders within the autoencoder architecture. These components consider both the user-item interaction matrix and the rows and columns of the item transition matrix. The reconstruction of the user-item interaction matrix captures user long-term preferences through collaborative filtering. In addition, the rows and columns of the item transition matrix represent the item out-degree and in-degree hopping behavior, which allows for modeling the user's short-term interests. When making incremental recommendations, only the input matrices need to be updated, without the need to update parameters, which makes AutoSeqRec very efficient. Comprehensive evaluations demonstrate that AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing its robustness and efficiency.

翻译：序列推荐通过建模用户的序列行为来展现推荐物品的能力。传统方法通常将用户视为物品序列，忽略了用户间的协同关系。图方法通过利用用户-物品交互图来融合协同信息，然而这类方法在时间复杂度和计算效率方面有时面临挑战。为解决这些局限，本文提出AutoSeqRec——一种专为序列推荐任务设计的增量式推荐模型。AutoSeqRec基于自编码器架构，包含一个编码器和三个解码器，这些组件同时考虑了用户-物品交互矩阵以及物品转移矩阵的行与列。用户-物品交互矩阵的重建通过协同过滤捕获用户的长期偏好；此外，物品转移矩阵的行与列分别表示物品的出度和入度跳转行为，从而能够对用户的短期兴趣进行建模。在进行增量推荐时，仅需更新输入矩阵而无需更新参数，这使得AutoSeqRec具有极高的效率。综合评估表明，AutoSeqRec在准确率方面优于现有方法，同时展现出其鲁棒性与高效性。