Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability of user preferences. An effective SR model is expected to capture both the long-term and short-term preferences exhibited by users, wherein the former can offer a comprehensive understanding of stable interests that impact the latter. To more effectively capture such information, we incorporate locality inductive bias into the Transformer by amalgamating its global attention mechanism with a local convolutional filter, and adaptively ascertain the mixing importance on a personalized basis through layer-aware adaptive mixture units, termed as AdaMCT. Moreover, as users may repeatedly browse potential purchases, it is expected to consider multiple relevant items concurrently in long-/short-term preferences modeling. Given that softmax-based attention may promote unimodal activation, we propose the Squeeze-Excitation Attention (with sigmoid activation) into SR models to capture multiple pertinent items (keys) simultaneously. Extensive experiments on three widely employed benchmarks substantiate the effectiveness and efficiency of our proposed approach. Source code is available at https://github.com/juyongjiang/AdaMCT.

翻译：序列推荐旨在从用户的一系列交互行为中建模其动态偏好。用户建模的关键挑战在于用户偏好固有的可变性。一个有效的序列推荐模型应能同时捕捉用户表现出的长期偏好与短期偏好，其中前者可提供对稳定兴趣的全面理解，进而影响后者。为更有效地捕获此类信息，我们通过将Transformer的全局注意力机制与局部卷积滤波器相结合，引入局部性归纳偏置，并通过基于层感知的自适应混合单元，以个性化方式自适应确定混合重要性，该方法被命名为AdaMCT。此外，由于用户可能重复浏览潜在购买商品，在长期/短期偏好建模中需同时考虑多个相关商品。鉴于基于Softmax的注意力机制可能促进单峰激活，我们提出将挤压-激励注意力（采用Sigmoid激活函数）引入序列推荐模型，以同时捕获多个相关键。在三个广泛使用的基准数据集上的大量实验验证了我们所提出方法的有效性与高效性。源代码见https://github.com/juyongjiang/AdaMCT。