# Enhancing User Intent for Recommendation Systems via Large Language Models

arXiv: https://arxiv.org/abs/2501.10871 · HTML: https://mlsanity.com/paper/2501.10871/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2025.

## Built on by (most cited first, 7 of 7)

- [Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions](https://mlsanity.com/paper/2502.00339/) (2025)
- [Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method](https://mlsanity.com/paper/2504.07983/) (2025)
- [E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction](https://mlsanity.com/paper/2506.04654/) (2025)
- [Radial Neighborhood Smoothing Recommender System](https://mlsanity.com/paper/2507.09952/) (2025)
- [AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization](https://mlsanity.com/paper/2603.21613/) (2026)
- [Fortress: A Case Study in Stabilizing Search Recommendations via Temporal Data Augmentation and Feature Pruning](https://mlsanity.com/paper/2605.15299/) (2026)
- [CRAMER: Control via Request-Aware Masking for Editing Recommenders](https://mlsanity.com/paper/2608.25370/) (2026)

## Abstract (arXiv)

Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), rely heavily on past user interactions or item features. However, these models often fail to capture the dynamic and evolving nature of user preferences. To address these limitations, we propose DUIP (Dynamic User Intent Prediction), a novel framework that combines LSTM networks with Large Language Models (LLMs) to dynamically capture user intent and generate personalized item recommendations. The LSTM component models the sequential and temporal dependencies of user behavior, while the LLM utilizes the LSTM-generated prompts to predict the next item of interest. Experimental results on three diverse datasets ML-1M, Games, and Bundle show that DUIP outperforms a wide range of baseline models, demonstrating its ability to handle the cold-start problem and real-time intent adaptation. The integration of dynamic prompts based on recent user interactions allows DUIP to provide more accurate, context-aware, and personalized recommendations. Our findings suggest that DUIP is a promising approach for next-generation recommendation systems, with potential for further improvements in cross-modal recommendations and scalability.