# Recommendations by Concise User Profiles from Review Text

arXiv: https://arxiv.org/abs/2311.01314 · HTML: https://mlsanity.com/paper/2311.01314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [TEARS: Textual Representations for Scrutable Recommendations](https://mlsanity.com/paper/2410.19302/) (2024)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [End-to-End Personalization: Unifying Recommender Systems with Large Language Models](https://mlsanity.com/paper/2508.01514/) (2025)
- [SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation](https://mlsanity.com/paper/2601.21105/) (2026)

## Abstract (arXiv)

Recommender systems perform well for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of users who have very sparse interactions but post informative review texts. This setting naturally calls for encoding user-specific text with large language models (LLM). However, feeding the full text of all reviews through an LLM has a weak signal-to-noise ratio and incurs high costs of processed tokens. This paper addresses these two issues. It presents a light-weight framework, called CUP, which first computes concise user profiles and feeds only these into the training of transformer-based recommenders. For user profiles, we devise various techniques to select the most informative cues from noisy reviews. Experiments, with book reviews data, show that fine-tuning a small language model with judiciously constructed profiles achieves the best performance, even in comparison to LLM-generated rankings.