Authors: Fei Tang, Yongliang Shen, Hang Zhang, Zeqi Tan, Wenqi Zhang, Guiyang Hou, Kaitao Song, Weiming Lu, Yueting Zhuang
Abstract: Large language model-based explainable recommendation (LLM-based ER) systems
show promise in generating human-like explanations for recommendations.
However, they face challenges in modeling user-item collaborative preferences,
personalizing explanations, and handling sparse user-item interactions. To
address these issues, we propose GaVaMoE, a novel Gaussian-Variational Gated
Mixture of Experts framework for explainable recommendation. GaVaMoE introduces
two key components: (1) a rating reconstruction module that employs Variational
Autoencoder (VAE) with a Gaussian Mixture Model (GMM) to capture complex
user-item collaborative preferences, serving as a pre-trained multi-gating
mechanism; and (2) a set of fine-grained expert models coupled with the
multi-gating mechanism for generating highly personalized explanations. The VAE
component models latent factors in user-item interactions, while the GMM
clusters users with similar behaviors. Each cluster corresponds to a gate in
the multi-gating mechanism, routing user-item pairs to appropriate expert
models. This architecture enables GaVaMoE to generate tailored explanations for
specific user types and preferences, mitigating data sparsity by leveraging
user similarities. Extensive experiments on three real-world datasets
demonstrate that GaVaMoE significantly outperforms existing methods in
explanation quality, personalization, and consistency. Notably, GaVaMoE
exhibits robust performance in scenarios with sparse user-item interactions,
maintaining high-quality explanations even for users with limited historical
data.
Source: http://arxiv.org/abs/2410.11841v1
