Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs

2025-11-23

AI Summary

Existing sequential recommendation methods often overlook the discontinuity of user interests—long-term interactions may not reflect current preferences—and the highly imbalanced temporal distribution of item interactions, which is influenced by global factors such as seasonality and events. To address these issues, this paper proposes a time-guided Graph Neural ODE framework. It innovatively constructs a user temporal graph and an item evolution graph, incorporates a time-guided diffusion generator to model dynamic temporal dependencies, and introduces a user interest truncation mechanism to suppress interference from irrelevant historical interactions. The framework synergistically integrates the structural modeling capability of graph neural networks with the continuous-time evolutionary modeling strength of ordinary differential equations, jointly capturing personalized interest evolution and global item distribution dynamics. Extensive experiments on five public benchmarks demonstrate that our model consistently outperforms state-of-the-art baselines by 10%–46% in recommendation accuracy.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc., revealing significant potential to enhance user experience. However, existing methods often overlook two critical factors: irregular user interests between interactions and highly uneven item distributions over time. The former factor implies that actual user preferences are not always continuous, and long-term historical interactions may not be relevant to current purchasing behavior. Therefore, relying only on these historical interactions for recommendations may result in a lack of user interest at the target time. The latter factor, characterized by peaks and valleys in interaction frequency, may result from seasonal trends, special events, or promotions. These externally driven distributions may not align with individual user interests, leading to inaccurate recommendations. To address these deficiencies, we propose TGODE to both enhance and capture the long-term historical interactions. Specifically, we first construct a user time graph and item evolution graph, which utilize user personalized preferences and global item distribution information, respectively. To tackle the temporal sparsity caused by irregular user interactions, we design a time-guided diffusion generator to automatically obtain an augmented time-aware user graph. Additionally, we devise a user interest truncation factor to efficiently identify sparse time intervals and achieve balanced preference inference. After that, the augmented user graph and item graph are fed into a generalized graph neural ordinary differential equation (ODE) to align with the evolution of user preferences and item distributions. This allows two patterns of information evolution to be matched over time. Experimental results demonstrate that TGODE outperforms baseline methods across five datasets, with improvements ranging from 10% to 46%.

Problem

• Modeling irregular user interest evolution between interactions
• Addressing uneven item distribution patterns over time
• Aligning temporal user preferences with item dynamics

Innovation

• Time-guided diffusion generator augments user graphs
• User interest truncation factor balances preference inference
• Graph neural ODE aligns user-item evolution patterns