Click-through rate (CTR) prediction is one of the core tasks in recommender systems. User behavior sequences, as one of the most effective features, can accurately reflect user preferences and significantly improve prediction accuracy. Richer behavior sequences often enable more comprehensive user profiling, and recent studies have shown that scaling the length of user behavior sequence can yield substantial gains in CTR. However, due to the widespread sparsity in recommender systems, incomplete behavior sequences are common in real-world scenarios. Existing sequential modeling methods often rely solely on the target user's own behavior, and therefore struggle in such scenarios. This paper proposes a novel method called SUIN (Similar Users-augmented Interest Network), which enhances the target user's behavior sequence with behaviors from similar users to enhance the user profile for CTR prediction. Specifically, we use behavior embeddings encoded by a sequence encoder to retrieve users with similar behaviors from a user retrieval pool. The behavior sequences of these similar users are then concatenated with that of the target user in descending order of similarity to construct an augmented sequence. Given that the augmented sequence contains behaviors from multiple users, we propose a user-specific target-aware position encoding, which identifies the source user of each behavior and captures its relative position to the target item. Furthermore, to mitigate the empirically observed noise in similar users' behaviors, we design a user-aware target attention that jointly considers item-item and user-user correlations, fully exploiting the potential of the augmented behavior sequence. Comprehensive experiments on widely-used short-term and long-term sequence benchmark datasets demonstrate that our method significantly outperforms state-of-the-art sequential CTR models.

翻译：点击率（CTR）预测是推荐系统中的核心任务之一。用户行为序列作为最有效的特征之一，能够准确反映用户偏好并显著提升预测精度。更丰富的行为序列通常能实现更全面的用户画像，而近期研究表明，扩展用户行为序列长度可大幅提升CTR表现。然而，由于推荐系统中普遍存在稀疏性，实际场景中常出现不完整的行为序列。现有序列建模方法通常仅依赖目标用户自身的行为，因此难以应对此类场景。本文提出一种名为SUIN（相似用户增强兴趣网络）的新方法，通过引入相似用户的行为来增强目标用户的行为序列，从而完善用户画像以提升CTR预测性能。具体而言，我们利用序列编码器生成的行为嵌入，从用户检索池中召回行为相似的用户。随后，将这些相似用户的行为序列按相似度降序拼接至目标用户行为序列，构建增强序列。针对增强序列包含多用户行为的特点，我们提出一种用户特定的目标感知位置编码机制，该机制可识别每个行为所属的源用户，并捕获其与目标项之间的相对位置关系。此外，为缓解实验中观察到的相似用户行为噪声问题，我们设计了用户感知的目标注意力机制，通过联合考虑项-项与用户-用户相关性，充分挖掘增强行为序列的潜力。在广泛采用的短期与长期序列基准数据集上的综合实验表明，我们的方法显著优于当前最先进的序列CTR模型。