Recommender systems improve reliability by adapting prompts to user risk
ReliGRec: Reliability-Oriented LLM-Based Generative Recommendation via User-Risk-Aware Prompt Routing
Information Retrieval
Summary
Online recommendation systems try to figure out what users like, but some users behave unpredictably or suspiciously, which can hurt recommendations. The authors designed a new system called ReliGRec that assesses a user's risk level by looking at their past behavior and feedback. Depending on this risk, it chooses different ways to ask a large language model for recommendations—either simple or cautious prompts—to make better and more reliable suggestions. This approach helps focus on stable, trustworthy information and improves recommendation quality.
What this means in practice
- •For ecommerce platform engineers: Add user-risk-based prompt routing to recommendation models to generate more reliable product suggestions despite unusual user behavior.$Commercial implications: Enables ecommerce platforms to sell more accurate recommendations, increasing customer satisfaction and revenue by improving reliability for users with erratic behavior.
- •For social media product teams: Integrate risk-aware prompting to reduce the influence of suspicious user activity on feed or content recommendations.
Authors
Haoran Yang, Fei Chen, Yutian Xiao, Jiahao Liang
Abstract
User behavior in real-world recommender systems is heterogeneous. While some users exhibit coherent preferences, others show abrupt interest shifts, bursty interactions, excessive repetition, or inconsistency with collaborative neighborhoods. Such deviations may arise from benign variation or manipulation, including shilling attacks, but do not alone establish malicious intent. Existing robust recommenders exploit user-risk signals through training-time reweighting or graph aggregation, whereas adapting generation to estimated user-level weak risk remains underexplored in LLM-based generative recommendation. We propose ReliGRec (Reliability-oriented Generative Recommendation), a weakly supervised framework whose name denotes its design goal rather than a supervised reliability variable. ReliGRec derives user-level weak-risk proxy labels from review-feedback signals for a subset of users and represents sequential behavior and collaborative context using a Behavior Token and temporal Graph Tokens, respectively. A Dual-View Weak-Risk Estimator fuses the representations to produce a user-level weak-risk score that selects a Simple or Cautious Prompt at inference. The Cautious Prompt is designed to encourage attention to stable, collaboratively supported evidence while reducing overreliance on isolated, short-term, or repeated interactions. The Behavior Token affects generation through weak-risk estimation and routing, whereas the aggregated Graph Token provides collaborative context for next-item Semantic ID generation. ReliGRec thus turns weak-risk estimation from an auxiliary prediction into a generation-time control signal. Experiments report competitive recommendation and weak-risk proxy-label prediction, while routing analyses characterize the recommendation-quality and inference-cost behavior of weak-risk-guided prompting.