In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.

翻译：在实际推荐场景中，用户常通过多种类型行为（如点击、加入购物车和购买）与物品交互。传统协同过滤技术通常假设用户与物品仅存在单一类型行为，导致难以充分利用复杂协同信号学习信息丰富的表征并推断用户真实偏好。为此，部分先驱研究探索对多行为异质性建模，以学习更优表征并提升目标行为推荐性能。然而，大量辅助行为（如点击和加入购物车）可能向推荐系统引入无关信息，从而误导目标行为（如购买）推荐，引发两个关键挑战：（i）辅助行为去噪，以及（ii）弥合辅助行为与目标行为之间的语义鸿沟。受上述观察启发，我们提出一种新颖框架——去噪与提示微调（DPT），采用三阶段学习范式解决上述挑战。具体而言，DPT在第一阶段配备模式增强图编码器，以数据驱动方式学习复杂模式作为先验知识，从而指导信息丰富表征的学习，并为后续阶段精准定位可靠噪声。随后阶段我们采用多种轻量级且高效的微调方法，进一步削弱噪声影响并缓解多类型行为间的语义鸿沟。在两个真实数据集上的大量实验表明，DPT相较于多种最新方法具有显著优越性。实现代码已在 https://github.com/zc-97/DPT 公开。