Review-Grounded Explainable Recommendation with Faithfulness Evaluation on Amazon Reviews - Xiaohan Chang; Yifei Lu; Ziliang Samuel Zhong (Fakultas Teknik Universitas Bhayangkara, 2026)
Review text can support explainable recommendations, but many recommender systems still optimize ranking accuracy without providing verifiable textual evidence, or they attach post-hoc explanations whose faithfulness to the model is unclear. This study addresses the lack of a reproducible evaluation setting that jointly measures recommendation quality and whether extracted review evidence actually supports model scoring. We propose Review-Grounded eXplainable Recommender (RGXRec), a lightweight hybrid method that combines interaction signals and TF-IDF review similarity, and we evaluate it on the Luxury Beauty and Video Games subsets of the Amazon Review Data. The pipeline includes rating thresholding, iterative 5-core pruning, chronological leave-one-out splitting, ranked recommendation, extractive evidence generation, and faithfulness evaluation. We compare RGXRec with popularity, metadata-graph KNN, SVD-MF, and ReviewSim using NDCG@K, Recall@K, MRR, evidence coverage, ROUGE-1, sentiment agreement, and a term-attribution faithfulness score. On Luxury Beauty, RGXRec achieves the best ranking performance, reaching NDCG@10 of 0.3606 and outperforming the strongest single-view baseline. On Video Games, collaborative and metadata signals remain stronger for ranking, but RGXRec preserves competitive accuracy while providing non-zero review-grounded faithfulness that interaction-only baselines cannot offer. These findings show that review-grounded recommendation should be evaluated on both ranking quality and explanation faithfulness.