Review-Grounded Explainable Recommendation with Faithfulness Evaluation on Amazon Reviews - Chang, Xiaohan; Lu, Yifei; Zhong, Ziliang Samuel (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.