Sequence-aware Large Language Models for Explainable Recommendation

TL;DR

SELLER is a novel framework that leverages sequence-aware modeling and utility-based evaluation to improve explainable recommendations generated by large language models.

Contribution

It introduces a sequence-aware LLM framework with a dual-path encoder and utility-aligned evaluation for better explanations and recommendation utility.

Findings

SELLER outperforms previous methods in explanation quality.

It improves recommendation utility in practical settings.

The framework effectively captures user behavior sequences.

Abstract

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.