In recommender systems, multi-behavior methods have demonstrated their effectiveness in mitigating issues like data sparsity, a common challenge in traditional single-behavior recommendation approaches. These methods typically infer user preferences from various auxiliary behaviors and apply them to the target behavior for recommendations. However, this direct transfer can introduce noise to the target behavior in recommendation, due to variations in user attention across different behaviors. To address this issue, this paper introduces a novel approach, Behavior-Contextualized Item Preference Modeling (BCIPM), for multi-behavior recommendation. Our proposed Behavior-Contextualized Item Preference Network discerns and learns users' specific item preferences within each behavior. It then considers only those preferences relevant to the target behavior for final recommendations, significantly reducing noise from auxiliary behaviors. These auxiliary behaviors are utilized solely for training the network parameters, thereby refining the learning process without compromising the accuracy of the target behavior recommendations. To further enhance the effectiveness of BCIPM, we adopt a strategy of pre-training the initial embeddings. This step is crucial for enriching the item-aware preferences, particularly in scenarios where data related to the target behavior is sparse. Comprehensive experiments conducted on four real-world datasets demonstrate BCIPM's superior performance compared to several leading state-of-the-art models, validating the robustness and efficiency of our proposed approach.

翻译：在推荐系统中，多行为方法已被证明在缓解传统单行为推荐方法中常见的数据稀疏性问题方面具有有效性。这些方法通常从各种辅助行为推断用户偏好，并将其应用于目标行为进行推荐。然而，由于不同行为中用户注意力的差异，这种直接迁移可能给推荐中的目标行为引入噪声。为解决此问题，本文提出了一种新颖方法——行为情境化物品偏好建模（BCIPM），用于多行为推荐。我们提出的行为情境化物品偏好网络能够辨别并学习每个行为中用户的特定物品偏好，随后仅考虑与目标行为相关的偏好进行最终推荐，显著降低了来自辅助行为的噪声。这些辅助行为仅用于训练网络参数，从而优化学习过程而不牺牲目标行为推荐的准确性。为进一步提升BCIPM的有效性，我们采用预训练初始嵌入的策略。这一步骤对于丰富物品感知偏好至关重要，特别是在目标行为数据稀疏的场景下。在四个真实数据集上进行的全面实验表明，BCIPM比多个领先的最先进模型具有更优的性能，验证了我们所提方法的鲁棒性和效率。