Attribute-aware sequential recommendation entails predicting the next item a user will interact with based on a chronologically ordered history of past interactions, enriched with item attributes. Existing methods typically leverage self-attention mechanisms to aggregate the entire sequence into a unified representation used for next-item prediction. While effective, these models often suffer from high computational complexity and memory consumption, limiting their ability to process long user histories. This constraint restricts the model's capacity to fully capture long-term user preferences. In some scenarios, modeling item interactions purely through attention may also not be the most effective approach to extract sequential patterns. In this work, we propose ConvRec, an alternative method with linear computational and memory complexity that employs convolutional layers in a hierarchical, down-scaled fashion to generate compact, yet expressive sequence representations. To further enhance the model's ability to capture diverse sequential patterns, each layer aggregates the neighboring items gradually to reach a comprehensive sequence representation. Extensive experiments on four real-world datasets demonstrate that our approach outperforms state-of-the-art sequential recommendation models, highlighting the potential of convolution-based architectures for efficient and effective sequence modeling in recommendation systems. Our implementation code and datasets are available here https://github.com/ismll-research/ConvRec.

翻译：属性感知的序列推荐旨在根据用户按时间顺序排列的历史交互记录（富含物品属性信息）预测用户下一步将交互的项目。现有方法通常利用自注意力机制将整个序列聚合为一个统一表示，用于预测下一项目。尽管这些模型有效，但常面临计算复杂度高和内存消耗大的问题，限制了其处理长用户历史序列的能力，进而削弱了模型充分捕捉长期用户偏好的潜力。此外，在某些场景下，仅通过注意力机制建模物品交互可能并非提取序列模式的最有效方法。本文提出ConvRec——一种具备线性计算和内存复杂度的替代方案。该方法采用分层下采样方式的卷积层生成紧凑且富有表达力的序列表示。为增强模型捕捉多样序列模式的能力，每个层逐渐聚合相邻物品，最终形成全面的序列表示。在四个真实数据集上的大量实验表明，我们的方法优于最先进的序列推荐模型，凸显了基于卷积的架构在推荐系统中实现高效序列建模的潜力。我们的实现代码与数据集已在https://github.com/ismll-research/ConvRec开源。