Review-Grounded Language Reranking for Personalized E-Commerce Recommendation with Faithful Explanation Evaluation
Keywords:
Personalized Recommendation, Review-Grounded Reranking, E-Commerce, Explanation Faithfulness, Amazon Reviews 2023, Evidence Extraction, Language ModelsAbstract
Personalized e-commerce recommendation increasingly depends on language-rich evidence: product titles, metadata, and user reviews. This paper studies a review-grounded reranking pipeline for a compact Amazon Reviews 2023 small-category slice, Gift Cards, where product choice often depends on occasion, delivery form, fees, and trust cues. The experiments use 152,410 review records and 1,137 metadata records; after one-review-per-user-item deduplication, the corpus contains 149,886 interactions from 132,732 users over 1,137 products. A chronological validation/test protocol holds out one future product per eligible user and ranks every one of the 1,137 products for each test user. The proposed Review-Grounded Language Reranker (RGLR) combines popularity, item-item collaborative filtering, metadata matching, review evidence similarity, and evidence quality. On 11,426 test users, RGLR reaches Hit@10=0.548, NDCG@10=0.356, and MRR@10=0.297, improving over the strongest single popularity baseline by 34.5% relative NDCG@10. The evidence extractor is also evaluated against the held-out review text: review-grounded evidence obtains profile relevance 0.139, rating agreement 0.902, and review support precision 0.502. For top-ranked recommendations, grounded explanations maintain complete source coverage and raise profile relevance from 0.033 for a helpful-review selector to 0.151. The results show that review grounding can improve ranking while giving faithful, auditable explanations.
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