Nizar Bouguila

Instruction-Tuned LLMs via Bayesian Mixture Model Preference Priors

Large language models (LLMs) deployed as interactive recommendation agents must adapt to user preferences across dialogue rounds without access to ground-truth reward functions. Existing Bayesian-prompting approaches assume a uniform prior over user types, discarding population structure and weakening cold-start perfor...

A Graph-Based Transfer Learning Approach for Short-Term Bus Passenger Flow Prediction

Bus passenger flow prediction is a crucial task in bus transportation management and optimization, characterized by strong spatiotemporal dependencies and influenced by external factors such as weather and holidays. However, existing methods face challenges in responding to sudden events, modeling dynamic changes in bu...

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

HyperXRec is 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, resulting in stable and semantically coherent clusters even under sparse interaction settings.

Bus passenger flow prediction with a dynamic graph neural network and bidirectional gated recurrent unit