Successfully defended my undergraduate thesis 🎓
I successfully defended my undergraduate thesis, “Leveraging Knowledge Graphs and Graph-Based Deep Learning Models for Recommendation Systems,” at Ho Chi Minh City University of Technology (HCMUT), under the supervision of Assoc. Prof. Dr. Thoai Nam, earning a final grade of 9.4/10. The graduation ceremony will be held in November 2026.
đź“‚ Thesis materials (report, slides): Google Drive
Abstract
Knowledge graph-based recommenders add semantic relations beyond user–item interactions, but most rely on fixed embeddings that fail to generalize to unseen items without retraining. Subgraph-based reasoning helps by recommending from local evidence around a user, yet full multi-hop expansion is costly and pulls in irrelevant nodes that hurt quality.
We propose Knowledge-aware Intent-guided Subgraph Sampling for Recommendation, which builds compact user-centric subgraphs by aligning node sampling with the user’s latent interests. Semantic attributes of the user’s interacted items are encoded into multiple personalized intents that guide adaptive sampling during multi-hop propagation and drive intent-aware item prediction, preserving preference-aligned evidence while cutting redundant expansion.
On the Last-FM and Amazon-Book benchmarks, under both traditional and new-item settings, the method matches strong subgraph-based baselines and outperforms collaborative filtering, embedding, GNN, and intent-agnostic sampling methods—while substantially reducing propagated messages, inference time, and GPU memory versus full subgraph reasoning. This shows intent-guided sampling balances accuracy and efficiency for inductive knowledge graph-based recommendation.