Sequential recommender systems aim to predict a user's future interests by extracting temporal patterns from their behavioral history. Existing approaches typically employ transformer-based architectures to process long sequences of user interactions, capturing preference shifts by modeling temporal relationships between items. However, these methods often overlook the influence of group-level features that capture the collective behavior of similar users. We hypothesize that explicitly modeling temporally evolving group features alongside individual user histories can significantly enhance next-item recommendation. Our approach introduces latent group representations, where each user's affiliation to these groups is modeled through learnable, time-varying membership weights. The membership weights at each timestep are computed by modeling shifts in user preferences through their interaction history, where we incorporate both short-term and long-term user preferences. We extract a set of statistical features that capture the dynamics of user behavior and further refine them through a series of transformations to produce the final drift-aware membership weights. A group-based representation is derived by weighting latent group embeddings with the learned membership scores. This representation is integrated with the user's sequential representation within the transformer block to jointly capture personal and group-level temporal dynamics, producing richer embeddings that lead to more accurate, context-aware recommendations. We validate the effectiveness of our approach through extensive experiments on five benchmark datasets, where it consistently outperforms state-of-the-art sequential recommendation methods.

翻译：序列推荐系统旨在通过提取用户行为历史中的时序模式来预测其未来兴趣。现有方法通常采用基于Transformer的架构处理用户交互的长序列，通过对物品间时序关系建模来捕捉偏好漂移。然而，这些方法往往忽略了捕获相似用户群体行为的群体级特征的影响。我们假设，在个体用户历史记录之外显式建模时序演化的群体特征，能够显著提升下一物品推荐性能。本方法引入潜在群体表征，其中每个用户与这些群体的从属关系通过可学习的时变隶属度权重进行建模。每个时间步的隶属度权重通过用户交互历史中的偏好漂移计算得到，在此过程中我们同时融合了短期与长期用户偏好。我们提取了一组捕获用户行为动态的统计特征，并通过一系列变换进一步优化这些特征，最终生成具有漂移感知能力的隶属度权重。基于群体的表征通过对潜在群体嵌入与习得的隶属度分数进行加权得到。该表征在Transformer模块中与用户的序列表征相融合，以共同捕捉个体层面与群体层面的时序动态，从而生成更丰富的嵌入表示，实现更精准、更具上下文感知能力的推荐。我们在五个基准数据集上通过大量实验验证了本方法的有效性，结果表明其性能始终优于当前最先进的序列推荐方法。