Knowledge Graph-augmented Sequential Recommendation with Adaptive Time-decay Kernels
DOI:
https://doi.org/10.13052/jwe1540-9589.2547Keywords:
Knowledge graph, sequential recommendation, temporal decay mechanism, self-attention mechanism, multi-task learning, feature fusionAbstract
To address the limitations of existing knowledge graph-enhanced recommendation systems – particularly their reliance on static fusion mechanisms that fail to capture the dynamic evolution of user interests and their inadequate modeling of heterogeneous information interactions – this paper proposes AdaTKGR, an adaptive time-decay weighted framework for knowledge graph-enhanced recommendations. First, a time-aware self-attention mechanism is introduced to effectively model temporal dependencies in user behavior sequences, thereby capturing fine-grained patterns of interest shift over time. Second, we integrate the RippleNet-style knowledge propagation strategy with a learnable temporal decay kernel, enabling dual-weighted representation learning based on both relational distance within the knowledge graph and temporal recency. Third, a cross-compression unit leveraging low-rank bilinear transformations is designed to facilitate deep semantic interaction between user–item interaction embeddings and knowledge graph entity representations. Finally, a time-gated multi-task learning objective is formulated to dynamically balance the primary recommendation task with auxiliary knowledge graph link prediction, enhancing joint optimization. Extensive experiments are conducted on three benchmark datasets – Book-Crossing, Last-FM, and MovieLens-1M – where AdaTKGR achieves average improvements of 6.1% and 8.4% in HR@10 and NDCG@10, respectively, over the strongest baseline methods. Notably, the proposed framework exhibits enhanced generalization performance and interpretability, particularly under data-sparse conditions. This work presents a principled approach to jointly optimizing temporal dynamics modeling and semantic knowledge integration in recommender systems.
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