# Instructional Prompt Optimization for Few-Shot LLM-Based Recommendations on Cold-Start Users

arXiv: https://arxiv.org/abs/2509.09066 · HTML: https://mlsanity.com/paper/2509.09066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 18 of 18)

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- [A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume](https://mlsanity.com/paper/2506.21927/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders](https://mlsanity.com/paper/2508.05289/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
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## Built on by (most cited first, 1 of 1)

- [RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations](https://mlsanity.com/paper/2608.19735/) (2026)

## Abstract (arXiv)

The cold-start user issue further compromises the effectiveness of recommender systems in limiting access to the historical behavioral information. It is an effective pipeline to optimize instructional prompts on a few-shot large language model (LLM) used in recommender tasks. We introduce a context-conditioned prompt formulation method P(u, Ds) rightarrow Rwidehat, where u is a cold-start user profile, Ds is a curated support set, and Rwidehat is the predicted ranked list of items. Based on systematic experimentation with transformer-based autoregressive LLMs (BioGPT, LLaMA-2, GPT-4), we provide empirical evidence that optimal exemplar injection and instruction structuring can significantly improve the precision@k and NDCG scores of such models in low-data settings. The pipeline uses token-level alignments and embedding space regularization with a greater semantic fidelity. Our findings not only show that timely composition is not merely syntactic but also functional as it is in direct control of attention scales and decoder conduct through inference. This paper shows that prompt-based adaptation may be considered one of the ways to address cold-start recommendation issues in LLM-based pipelines.