Federated Recommendation (FedRec) enables privacy-preserving recommendation by training models across user devices without sharing raw interaction data. However, personalized FedRec still faces a trade-off between personalization and resource efficiency. In this paper, we revisit personalization from the perspective of the local optimization horizon. We find that sufficiently long local optimization can form user-specific item structures, but frequent synchronization or full replacement can disrupt them. Based on this observation, we propose
FedRKG
, a model-agnostic framework that forms personalized item embeddings through
Long-Horizon Local Optimization
and injects common global knowledge through intermittent
Regularized Knowledge Guidance
. This design incorporates shared collaborative signals without replacing local embeddings or maintaining dual representations. We further introduce
Adaptive Guidance
to control the influence of global knowledge at the user–item interaction level. Extensive experiments show that FedRKG consistently outperforms state-of-the-art FedRec baselines while reducing storage and communication costs. Our code is available at
https://github.com/Jaehyung-Lim/FedRKG_JIIS26_SCIE
.
Jaehyung Lim, Wonbin Kweon, Woojoo Kim et al.· Journal of Intelligence and...· 0 citations
Large language models (LLMs) have transformed AI, yet they remain fundamentally limited by hallucination, unverifiable reasoning, and shallow evidence grounding. We argue that structure mining-rooted in decades of KDD research on taxonomy induction, ontology design, entity typing, and knowledge graph construction-is the key to overcoming these limitations. This tutorial presents a unified vision in which structuring serves as the enabling foundation for three pillars of next-generation LLM systems: (1) Structured Retrieval, where organizing corpora into ontology-guided multidimensional representations enables SQL-like queries that achieve substantially more precise and complete retrieval than similarity-based approaches; (2) Structured Reasoning, where grounding each inference step in typed, graph-structured evidence transforms opaque generation into auditable, verifiable reasoning chains; and (3) Structured Agent Memory, where multi-dimensional memory architectures bridge external corpus knowledge and experiential agent knowledge through a mutually enriching dual-memory design. Across all three pillars, we highlight how the cooperative interplay between classical KDD techniques and modern LLMs-where KDD defines structural schemas and quality constraints while LLMs execute flexible extraction and reasoning-creates systems that are more reliable, interpretable, and faithful. The tutorial covers both foundational methods and the latest advances (2024--2026), and concludes with open problems and future research directions at the intersection of data mining and LLMs.
Pengcheng Jiang, Jiashuo Sun, Wonbin Kweon et al.· Proceedings of the 32nd ACM...· 0 citations
Federated recommendation (FedRec) enables personalized modeling without centralizing users'interaction histories, but most existing methods assume a fixed item pool and thus overlook the practical cold-item setting where new items continuously arrive. Under the dual-sided constraint, where the server cannot access clients'interactions while clients cannot access the server's proprietary item attribute features, prior federated cold-start recommendation approaches suffer from three structural limitations: a lack of personalization, compositionality failure caused by forcing heterogeneous semantics into a single embedding space, and training- and communication-inefficiency arising from explicit alignment between separate collaborative and attribute representations. To address these challenges, we propose Personalized and Multi-view Representation for Federated Cold-Start Recommendation (PMFRec). PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy. In addition, PMFRec fuses collaborative and attribute knowledge into a single exchanged item representation, eliminating the need for an explicit client-side regularizer and reducing communication overhead. Extensive experiments on real-world datasets show that PMFRec consistently outperforms strong baselines in cold-item recommendation and further improves user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy (LDP).
Jaehyung Lim, Wonbin Kweon, Woojoo Kim et al.· 0 citations