JARVIS: An Evidence-Grounded Retrieval System for Interpretable Deceptive Reviews Adjudication
- URL: http://arxiv.org/abs/2602.12941v1
- Date: Fri, 13 Feb 2026 13:57:45 GMT
- Title: JARVIS: An Evidence-Grounded Retrieval System for Interpretable Deceptive Reviews Adjudication
- Abstract summary: JARVIS is a framework providing Judgment via Augmented Retrieval and eVIdence graph Structures.<n>It retrieves semantically similar evidence via hybrid dense-sparse multimodal retrieval, expands relational signals through shared entities, and constructs a heterogeneous evidence graph.<n>Our framework achieves a 27% increase in the recall volume and reduces manual inspection time by 75%.
- Score: 4.797952842010917
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deceptive reviews, refer to fabricated feedback designed to artificially manipulate the perceived quality of products. Within modern e-commerce ecosystems, these reviews remain a critical governance challenge. Despite advances in review-level and graph-based detection methods, two pivotal limitations remain: inadequate generalization and lack of interpretability. To address these challenges, we propose JARVIS, a framework providing Judgment via Augmented Retrieval and eVIdence graph Structures. Starting from the review to be evaluated, it retrieves semantically similar evidence via hybrid dense-sparse multimodal retrieval, expands relational signals through shared entities, and constructs a heterogeneous evidence graph. Large language model then performs evidence-grounded adjudication to produce interpretable risk assessments. Offline experiments demonstrate that JARVIS enhances performance on our constructed review dataset, achieving a precision increase from 0.953 to 0.988 and a recall boost from 0.830 to 0.901. In the production environment, our framework achieves a 27% increase in the recall volume and reduces manual inspection time by 75%. Furthermore, the adoption rate of the model-generated analysis reaches 96.4%.
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