Sequence-aware Large Language Models for Explainable Recommendation
This addresses the need for more practical and effective explainable recommender systems for users and developers, representing an incremental improvement over existing LLM-based approaches.
The paper tackles the problem that existing LLM-based explainable recommendation methods overlook sequential user behavior dynamics and use misaligned evaluation metrics, proposing SELLER which integrates explanation generation with utility-aware evaluation. Experiments show SELLER consistently outperforms prior methods in explanation quality and real-world utility on public benchmarks.
Large Language Models (LLMs) have shown strong potential in generating natural language explanations for recommender systems. However, existing methods often overlook the sequential dynamics of user behavior and rely on evaluation metrics misaligned with practical utility. We propose SELLER (SEquence-aware LLM-based framework for Explainable Recommendation), which integrates explanation generation with utility-aware evaluation. SELLER combines a dual-path encoder-capturing both user behavior and item semantics with a Mixture-of-Experts adapter to align these signals with LLMs. A unified evaluation framework assesses explanations via both textual quality and their effect on recommendation outcomes. Experiments on public benchmarks show that SELLER consistently outperforms prior methods in explanation quality and real-world utility.