Identity cues can bias recommendations but drift can be reduced
Measuring and Mitigating Identity-Cue Preference Drift in LLM-based Recommender Systems
Information RetrievalSocial and Information Networks
Summary
Recommendations from large language models can be unintentionally biased when commands include identity information, causing them to rely more on group trends than individual interests. The authors developed PromptShift, a way to measure how much these identity cues change the recommendations and a method to correct the bias without retraining the model. They tested their approach on multiple datasets and models, showing it reduces bias while keeping recommendation quality. This helps to make LLM-powered recommendation systems more personalized and fair.
What this means in practice
- •For recommendation system engineers: Identify and correct bias introduced by identity information in LLM recommendations without retraining models.
- •For product managers for personalization: Improve fairness and relevance of personalized content by mitigating identity-driven recommendation shifts.
Authors
Zhuoxiong Gan, Qiang Dong
Abstract
In large language model-based recommender systems, identity cues embedded in prompts can steer recommendations toward group-level patterns even when the underlying behavioral evidence remains unchanged. We introduce PromptShift, an interpretable, training-free framework for quantifying and mitigating such identity-cue preference drift. We define Drift as the divergence, in both item membership and ranking order, between a recommendation list generated under an identity-cued prompt and the reference list produced from the same user's interaction history alone. SliceShift then measures the extent to which a cued list gravitates, relative to the history-only reference, toward items that are more popular within the cued slice than among the global user population. Beyond conventional accuracy, we propose DifHitRate, a difficulty-weighted hit metric that credits only relevant items, assigning higher credit to hits that are less popular within the cued slice and ranked higher in the list. All components are supported by an identity-slice-by-item table constructed from positive interactions, which further enables an adaptive post-hoc reranking strategy: the reranker interpolates between the original LLM ranking and inverse slice-popularity, with personalized interpolation weight. Experiments on two datasets with three LLMs show that identity-cued prompts incur higher mean Drift than identity-free paraphrase controls, an effect beyond generic wording sensitivity, and that SliceShift is positive across all six dataset-model settings. PromptShift consistently reduces both Drift and SliceShift, lowering macro-mean SliceShift by 62.42%, while improving DifHitRate, HitRate and MRR. These results demonstrate that identity-cue preference drift can be measured and mitigated without any model training, albeit with a modest, metric-dependent utility cost.