{"type":"ScholarlyArticle","title":"Geodesic Expert Routing for Unbiased Knowledge Distillation in Recommendation","authors":[{"name":"Xuan Zhang","slug":"xuan-zhang","given":"Xuan","family":"Zhang","affiliation":""},{"name":"Rongchuan Wei","slug":"rongchuan-wei","given":"Rongchuan","family":"Wei","affiliation":""},{"name":"Chunyu Wei","slug":"chunyu-wei","given":"Chunyu","family":"Wei","affiliation":""},{"name":"Hongxing Yuan","slug":"hongxing-yuan","given":"Hongxing","family":"Yuan","affiliation":""},{"name":"Yushun Fan","slug":"yushun-fan","given":"Yushun","family":"Fan","affiliation":""}],"abstract":"Knowledge distillation has become a prevalent technique for deploying efficient recommender systems, enabling lightweight student models to approximate the performance of larger teachers. However, we identify a critical issue: distillation systematically amplifies popularity bias, as student models inherit and intensify the popularity-driven shortcuts encoded in teachers trained on interaction data dominated by popular items. To address this limitation, we propose GUIDE (Geodesic aware Unbiased Instructive Distillation with Experts), a collaborative distillation framework that incorporates domain-specific debiasing experts alongside the global teacher. GUIDE tackles two key challenges in this paradigm. First, for expert routing, we introduce Spherical Expert Alignment, which conducts expert-student matching on the spherical manifold with geodesic distance optimization, eliminating magnitude-induced bias and ensuring stable gradient flow. Second, for context fusion, we design a Meta-Debiasing Gate that dynamically arbitrates teacher-expert influence based on real-time user-item context through end-to-end meta-learning. Extensive experiments on multiple real-world datasets demonstrate that GUIDE significantly mitigates popularity bias while preserving recommendation accuracy, with state-of-the-art trade-offs among efficiency, accuracy, and fairness. The code and data are available at:https://github.29com/zx19971219/GUIDE.","doi":"10.24963/ijcai.2026/361","url":"https://ijcai.org/proceedings/2026/361","pdf":"https://ijcai.org/proceedings/2026/0361.pdf","markdown":"https://ijcai.org/proceedings/2026/361.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/361","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":361,"pages":{"first":3250,"last":3258},"keywords":["Data Mining: Recommender systems","Machine Learning: Deep learning architectures"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}