Multi-view contrastive learning with Static attributes and Dynamic interests for Sequential Recommendation

Sequential recommendation plays a critical role in preference prediction by capturing the temporal evolution of user behavior. However, a key challenge lies in effectively integrating static attributes, such as stable user traits and item properties, with dynamic interests, which reflect the users’ transient and evolving preferences during interactions with various items. Current approaches typically focus on static attributes or recent interactions, neglecting the nuanced interplay between long-term stability and short-term variability. Additionally, the disparate encoding strategies for various data structures—such as bipartite interaction graphs, heterogeneous knowledge graphs, and sequential data streams—lead to fragmented user and item representations, hindering the development of a unified framework and reducing the system’s ability to holistically model user preferences. To address these challenges, we propose the multi-view contrastive learning with Static attributes and Dynamic interests for Sequential Recommendation (SDSR), a novel framework that integrates static and dynamic characteristics to enhance recommendation systems. SDSR employs graph-based encoders to capture static user and item features, while a sequence encoder models temporal changes in user behavior. By leveraging contrastive learning, SDSR aligns representations across multiple data views—such as interaction graphs, knowledge graphs, and sequential data—creating a unified user-item model that bridges long-term preferences with short-term trends. It also ensures consistency across various representations, yielding a cohesive and robust framework for synthesizing multi-perspective data. Empirical evaluations on benchmark datasets demonstrate that SDSR significantly outperforms state-of-the-art models, validating its effectiveness in integrating multi-view data and capturing both static and dynamic user preferences.