{"ID":2840346,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12959","arxiv_id":"2511.12959","title":"Personalized Federated Recommendation With Knowledge Guidance","abstract":"Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory-efficient single-knowledge models suffer from a suboptimal knowledge replacement practice that discards valuable personalization, while high-performance dual-knowledge models are often too memory-intensive for practical on-device deployment. We propose Federated Recommendation with Knowledge Guidance (FedRKG), a model-agnostic framework that resolves this dilemma. The core principle, Knowledge Guidance, avoids full replacement and instead fuses global knowledge into preserved local embeddings, attaining the personalization benefits of dual-knowledge within a single-knowledge memory footprint. Furthermore, we introduce Adaptive Guidance, a fine-grained mechanism that dynamically modulates the intensity of this guidance for each user-item interaction, overcoming the limitations of static fusion methods. Extensive experiments on benchmark datasets demonstrate that FedRKG significantly outperforms state-of-the-art methods, validating the effectiveness of our approach. The code is available at https://github.com/Jaehyung-Lim/fedrkg.","short_abstract":"Federated Recommendation (FedRec) has emerged as a key paradigm for building privacy-preserving recommender systems. However, existing FedRec models face a critical dilemma: memory-efficient single-knowledge models suffer from a suboptimal knowledge replacement practice that discards valuable personalization, while hig...","url_abs":"https://arxiv.org/abs/2511.12959","url_pdf":"https://arxiv.org/pdf/2511.12959v2","authors":"[\"Jaehyung Lim\",\"Wonbin Kweon\",\"Woojoo Kim\",\"Junyoung Kim\",\"Dongha Kim\",\"Hwanjo Yu\"]","published":"2025-11-17T04:35:53Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[]","has_code":false,"code_links":[{"ID":606960,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840346,"paper_url":"https://arxiv.org/abs/2511.12959","paper_title":"Personalized Federated Recommendation With Knowledge Guidance","repo_url":"https://github.com/Jaehyung-Lim/fedrkg","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}