Multi-behavior recommendation aims to integrate users' interactions across various behavior types (e.g., view, favorite, add-to-cart, purchase) to more comprehensively characterize user preferences. However, existing methods lack in-depth modeling when dealing with interactions that generate only auxiliary behaviors without triggering the target behavior. In fact, these weak signals contain rich latent information and can be categorized into two types: (1) positive weak signals-items that have not triggered the target behavior but exhibit frequent auxiliary interactions, reflecting users' hesitation tendencies toward these items; and (2) negative weak signals-auxiliary behaviors that result from misoperations or interaction noise, which deviate from true preferences and may cause negative transfer effects. To more effectively identify and utilize these weak signals, we propose a recommendation framework focused on weak signal learning, termed HNT. Specifically, HNT models weak signal features from two dimensions: positive and negative effects. By learning the characteristics of auxiliary behaviors that lead to target behaviors, HNT identifies similar auxiliary behaviors that did not trigger the target behavior and constructs a hesitation set of related items as weak positive samples to enhance preference modeling, thereby capturing users' latent hesitation intentions. Meanwhile, during auxiliary feature fusion, HNT incorporates latent negative transfer effect modeling to distinguish and suppress interference caused by negative representations through item similarity learning. Experiments on three real-world datasets demonstrate that HNT improves HR@10 and NDCG@10 by 12.57% and 14.37%, respectively, compared to the best baseline methods.

翻译：多行为推荐旨在整合用户在不同行为类型（如浏览、收藏、加入购物车、购买）上的交互，以更全面地刻画用户偏好。然而，现有方法在处理仅产生辅助行为而未触发目标行为的交互时缺乏深入建模。实际上，这些弱信号蕴含丰富的潜在信息，可分为两类：（1）正向弱信号——未触发目标行为但呈现频繁辅助交互的物品，反映用户对这些物品的犹豫倾向；（2）负向弱信号——由误操作或交互噪声产生的辅助行为，偏离真实偏好并可能引发负迁移效应。为更有效地识别与利用这些弱信号，我们提出一种专注于弱信号学习的推荐框架HNT。具体而言，HNT从正负效应两个维度建模弱信号特征：通过学习导致目标行为的辅助行为特征，HNT识别未触发目标行为的相似辅助行为，构建相关物品的犹豫集作为弱正样本以增强偏好建模，从而捕捉用户的潜在犹豫意图。同时，在辅助特征融合过程中，HNT引入潜在负迁移效应建模，通过物品相似性学习区分并抑制负向表征带来的干扰。在三个真实数据集上的实验表明，相较于最佳基线方法，HNT在HR@10和NDCG@10指标上分别提升了12.57%和14.37%。