SFTe: Temporal knowledge graphs embedding for future interaction prediction

• Published in: Information systems (Oxford) Vol. 125; p. 102423
• Main Authors: Jia, Wei, Ma, Ruizhe, Niu, Weinan, Yan, Li, Ma, Zongmin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Interaction networks; Multi-relations; Temporal knowledge graph; Time-aware interaction prediction; Interaction networks; Multi-relations; Time-aware interaction prediction; Temporal knowledge graph
• ISSN: 0306-4379; 1873-6076
• DOI: 10.1016/j.is.2024.102423

Interaction prediction is a crucial task in the Social Internet of Things (SIoT), serving diverse applications including social network analysis and recommendation systems. However, the dynamic nature of items, users, and their interactions over time poses challenges in effectively capturing and analyzing these changes. Existing interaction prediction models often overlook the temporal aspect and lack the ability to model multi-relational user-item interactions over time. To address these limitations, in this paper, we propose a Structure, Facticity, and Temporal information preservation embedding model (SFTe) to predict future interaction. Our model leverages the advantages of Temporal Knowledge Graphs (TKGs) that can capture both the multi-relations and evolution. We begin by modeling user-item interactions over time by constructing a Temporal Interaction Knowledge Graph (TIKG). We then employ Structure Embedding (SE), Facticity Embedding (FE), and Temporal Embedding (TE) to capture topological structure, facticity consistency, and temporal dependence, respectively. In SE, we focus on preserving the first-order relationships to capture the topological structure of TIKG. In the FE component, given the distinct nature of SIoT, we introduce an attention mechanism to capture the effect of entities with the same additional information for generating subgraph embeddings. Lastly, TE utilizes recurrent neural networks to model the temporal dependencies among subgraphs and capture the evolving dynamics of the interactions over time. Experimental results on standard future interaction prediction demonstrate the superiority of the SFTe model compared with the state-of-the-art methods. Our model effectively addresses the challenges of time-aware interaction prediction, showcasing the potential of TKGs to enhance prediction performance.