HyperXRec: Unifying Preference Clusters and LLM Experts for Robust Explainable Recommendations

Abstract

Explainable recommendation is crucial for building user trust, yet producing natural-language rationales that faithfully reflect the underlying decision process remains challenging. Most LLM-based explainable recommenders incorporate collaborative signals through shallow prompting or lightweight adapters, which often yields generic explanations that are only loosely grounded in preference evidence, particularly when interactions are sparse. To address this gap, we propose HyperXRec, a novel framework that unifies preference modeling and explanation generation by integrating hyperspherical latent clustering with a cluster-guided mixture-of-experts (MoE) inside an LLM. Specifically, HyperXRec employs a dual-VAE with a von Mises-Fisher mixture prior to learn robust hyperspherical user/item embeddings, resulting in stable and semantically coherent clusters even under sparse interaction settings. These clusters are subsequently leveraged to guide fine-grained expert routing in the LLM, encouraging explanations that are both personalized and explicitly aligned with the learned preference structure. Experiments on multiple benchmarks demonstrate that HyperXRec consistently outperforms strong baselines in explanation fidelity, personalization, and robustness in cold-start scenarios.