Sequential Recommendation is a prominent topic in current research, which uses user behavior sequence as an input to predict future behavior. By assessing the correlation strength of historical behavior through the dot product, the model based on the self-attention mechanism can capture the long-term preference of the sequence. However, it has two limitations. On the one hand, it does not effectively utilize the items' local context information when determining the attention and creating the sequence representation. On the other hand, the convolution and linear layers often contain redundant information, which limits the ability to encode sequences. In this paper, we propose a self-attentive sequential recommendation model based on cheap causal convolution. It utilizes causal convolutions to capture items' local information for calculating attention and generating sequence embedding. It also uses cheap convolutions to improve the representations by lightweight structure. We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation. Experiments on benchmark datasets show that the proposed model can perform better in single- and multi-objective recommendation scenarios.

翻译：序列推荐是当前研究中的一个重要课题，它利用用户行为序列作为输入来预测未来行为。通过点积评估历史行为的相关性强弱，基于自注意力机制的模型能够捕捉序列的长期偏好。然而，该模型存在两个局限性：一方面，在确定注意力权重并生成序列表示时，未能有效利用项目的局部上下文信息；另一方面，卷积层和线性层通常包含冗余信息，限制了序列编码的能力。本文提出了一种基于廉价因果卷积的自注意力序列推荐模型。该模型利用因果卷积捕捉项目的局部信息，以计算注意力并生成序列嵌入，同时通过轻量级结构采用廉价卷积改进表示。我们从准确度和校准度两方面评估了所提模型在序列推荐中的有效性。基准数据集上的实验表明，该模型在单目标和多目标推荐场景中均能取得更优性能。