Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platforms. However, many recommendation models rely on a single type of behavior learning, which limits their ability to represent the complex relationships between users and items in real-life scenarios. In such situations, users interact with items in multiple ways, including clicking, tagging as favorite, reviewing, and purchasing. To address this issue, we propose the Relation-aware Contrastive Learning (RCL) framework, which effectively models dynamic interaction heterogeneity. The RCL model incorporates a multi-relational graph encoder that captures short-term preference heterogeneity while preserving the dedicated relation semantics for different types of user-item interactions. Moreover, we design a dynamic cross-relational memory network that enables the RCL model to capture users' long-term multi-behavior preferences and the underlying evolving cross-type behavior dependencies over time. To obtain robust and informative user representations with both commonality and diversity across multi-behavior interactions, we introduce a multi-relational contrastive learning paradigm with heterogeneous short- and long-term interest modeling. Our extensive experimental studies on several real-world datasets demonstrate the superiority of the RCL recommender system over various state-of-the-art baselines in terms of recommendation accuracy and effectiveness.

翻译：个性化推荐系统在捕捉用户随时间演变的偏好方面发挥着关键作用，为各类在线平台提供精准有效的推荐。然而，许多推荐模型仅依赖单一类型的行为学习，限制了其在真实场景中表征用户与物品间复杂关系的能力。在实际情境中，用户与物品存在多种交互方式，包括点击、收藏、评论和购买。为解决这一问题，我们提出关系感知对比学习框架，该框架有效建模动态交互异质性。模型采用多关系图编码器，在保留不同用户-物品交互类型特有语义关系的同时，捕捉短期偏好异质性。此外，我们设计了动态跨关系记忆网络，使RCL模型能够捕捉用户长期的多行为偏好以及随时间演变的跨类型行为依赖关系。为获得兼具共性与多样性的鲁棒用户表征，我们引入了多关系对比学习范式，融合异质短期与长期兴趣建模。在多个真实数据集上的大量实验结果表明，与各类最先进基线方法相比，RCL推荐系统在推荐准确性和有效性方面均具有显著优势。