AdaRec: Adaptive Recommendation with LLMs via Narrative Profiling and Dual-Channel Reasoning

• URL: http://arxiv.org/abs/2511.07166v1
• Date: Mon, 10 Nov 2025 14:59:27 GMT
• Title: AdaRec: Adaptive Recommendation with LLMs via Narrative Profiling and Dual-Channel Reasoning
• Authors: Meiyun Wang, Charin Polpanumas,
• Abstract summary: AdaRec is a few-shot in-context learning framework that leverages large language models for an adaptive personalized recommendation.<n>AdaRec employs a dual-channel architecture that integrates horizontal behavioral alignment, discovering peer-driven patterns, with vertical causal attribution.<n>Experiments on real ecommerce datasets demonstrate that AdaRec outperforms both machine learning models and LLM-based baselines.
• Score: 4.45755699416829
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose AdaRec, a few-shot in-context learning framework that leverages large language models for an adaptive personalized recommendation. AdaRec introduces narrative profiling, transforming user-item interactions into natural language representations to enable unified task handling and enhance human readability. Centered on a bivariate reasoning paradigm, AdaRec employs a dual-channel architecture that integrates horizontal behavioral alignment, discovering peer-driven patterns, with vertical causal attribution, highlighting decisive factors behind user preferences. Unlike existing LLM-based approaches, AdaRec eliminates manual feature engineering through semantic representations and supports rapid cross-task adaptation with minimal supervision. Experiments on real ecommerce datasets demonstrate that AdaRec outperforms both machine learning models and LLM-based baselines by up to eight percent in few-shot settings. In zero-shot scenarios, it achieves up to a nineteen percent improvement over expert-crafted profiling, showing effectiveness for long-tail personalization with minimal interaction data. Furthermore, lightweight fine-tuning on synthetic data generated by AdaRec matches the performance of fully fine-tuned models, highlighting its efficiency and generalization across diverse tasks.

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