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基于用户过去按时间顺序交互的历史记录（并辅以项目属性）预测用户下一个可能交互的项目。现有方法通常利用自注意力机制将整个序列聚合为统一的表示，用于下一个项目的预测。尽管这些模型有效，但它们往往面临高计算复杂度和内存消耗的问题，限制了处理长用户历史的能力。这种约束削弱了模型充分捕捉长期用户偏好的能力。在某些场景中，仅通过注意力机制建模项目交互可能并非提取序列模式的最有效方法。在本工作中，我们提出ConvRec，一种具有线性和计算与内存复杂度的替代方法，该方法以分层、降尺度的方式使用卷积层来生成紧凑且表达力强的序列表示。为进一步增强模型捕捉多样序列模式的能力，每层通过逐步聚合邻近项目，最终得到全面的序列表示。在四个真实世界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，突显了基于卷积架构在推荐系统中实现高效且有效序列建模的潜力。我们的实现代码和数据集可在https://github.com/ismll-research/ConvRec获取。