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变化的偏好方面发挥着关键作用，能够在各类在线平台上提供准确且有效的推荐。然而，许多推荐模型依赖单一类型的行为学习，这限制了它们在真实场景中表示用户与物品之间复杂关系的能力。在此类场景中，用户通过多种方式与物品进行交互，包括点击、标记为收藏、评论以及购买。为解决这一问题，我们提出了关系感知对比学习（RCL）框架，该框架能够有效建模动态交互的异质性。RCL模型融合了多关系图编码器，可在保留不同用户-物品交互类型所蕴含的关系语义的同时，捕捉短期偏好异质性。此外，我们设计了一个动态跨关系记忆网络，使RCL模型能够捕获用户的长期多行为偏好以及随时间演变的底层跨类型行为依赖关系。为获得兼具共识性与多样性的鲁棒且信息丰富的用户表征，我们引入了一种多关系对比学习范式，该范式结合了异质的短期与长期兴趣建模。我们在多个真实世界数据集上进行的大量实验研究表明，RCL推荐系统在推荐准确性和有效性方面优于多种当前最优基线模型。