Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback. To address the above problems, we propose multi-behavior alignment (MBA), a novel recommendation framework that learns from implicit feedback by using multiple types of behavioral data. We conjecture that multiple types of behavior from the same user (e.g., clicks and purchases) should reflect similar preferences of that user. To this end, we regard the underlying universal user preferences as a latent variable. The variable is inferred by maximizing the likelihood of multiple observed behavioral data distributions and, at the same time, minimizing the Kullback-Leibler divergence (KL-divergence) between user models learned from auxiliary behavior (such as clicks or views) and the target behavior separately. MBA infers universal user preferences from multi-behavior data and performs data denoising to enable effective knowledge transfer. We conduct experiments on three datasets, including a dataset collected from an operational e-commerce platform. Empirical results demonstrate the effectiveness of our proposed method in utilizing multiple types of behavioral data to enhance the prediction of the target behavior.

翻译：从隐式反馈中学习的推荐系统通常利用大量单一类型的隐式用户反馈（例如点击）来增强稀疏目标行为（例如购买）的预测。利用多种类型的隐式用户反馈进行目标行为预测仍是一个开放性问题。现有尝试学习多种类型用户行为的研究通常存在以下不足：（i）难以从不同行为数据分布中学习普适且准确的用户偏好；（ii）难以克服观测到的隐式用户反馈中的噪声与偏差。为解决上述问题，我们提出多行为对齐框架（MBA），一种通过利用多种类型行为数据从隐式反馈中学习的新型推荐框架。我们假设同一用户的多类型行为（例如点击与购买）应反映该用户的相似偏好。为此，我们将潜在的普适用户偏好视为隐变量，通过最大化多个观测行为数据分布的似然，同时最小化从辅助行为（如点击或浏览）与目标行为分别学习的用户模型之间的KL散度（Kullback-Leibler散度）来推断该变量。MBA从多行为数据中推断普适用户偏好，并执行数据去噪以促进有效的知识迁移。我们在三个数据集（包括从运营中电商平台收集的数据集）上开展实验，实证结果证明了所提方法在利用多类型行为数据提升目标行为预测方面的有效性。