Continual knowledge graph embedding enhancement for joint interaction-based next click recommendation

• Published in: Knowledge-based systems Vol. 304; p. 112475
• Main Authors: Khan, Nasrullah, Ma, Zongmin, Ma, Ruizhe, Polat, Kemal
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 25.11.2024
• Subjects: Catastrophic forgetting; Continual KGE enhancement; Interactions to quadruplets; Next click recommendation; Next click recommendation; Catastrophic forgetting; Continual KGE enhancement; Interactions to quadruplets
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2024.112475

Knowledge Graph Embedding (KGE) based deep neural networks contribute to recommender systems in diverse application scenarios. However, Catastrophic Forgetting (CForg) significantly degrades their performance. Although exemplar replay is commonly adopted as a possible remedy to alleviate the intensity of CForg, a trade-off between performance and complexity occurs in this process. Therefore, in this work, we introduce Continual Knowledge graph embedding enhancement for joint Interaction-based Next click recommendation (CKIN) to defy the CForg and assuage the complexity. Typically, we introduce the Semantic Relevance Estimation (SRE) technique to ensure information relevance by filtering out irrelevant-data and reducing the space complexity. We introduce the SRE-enhanced deep probabilistic technique to probably replay the most relevant exemplars to defy the CForg and reduce the time complexity. Moreover, we introduce the integration of locality-preserving loss into the KGE framework to optimize the loss. In substantial experiments on real-world datasets, CKIN outperforms the baseline methods by effectively meeting the highlighted challenges. •Introduce SRE to automatically store relevant exemplars.•Introduce SDP to probabilistically replay the exemplars.•Introduce integration of LPL as a distillation-loss into the BCE loss.•Design CGNN to learn representations and continually update KGEB.•CKIN outperforms the baseline methods.