# ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users

arXiv: https://arxiv.org/abs/2510.09393 · HTML: https://mlsanity.com/paper/2510.09393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation](https://mlsanity.com/paper/1908.00413/) (2019)
- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [Improving Recommendation Fairness via Data Augmentation](https://mlsanity.com/paper/2302.06333/) (2023)
- [MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation](https://mlsanity.com/paper/2304.08382/) (2023)
- [R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems](https://mlsanity.com/paper/2507.17249/) (2025)
- [Graph Intention Network for Click-through Rate Prediction in Sponsored Search](https://mlsanity.com/paper/2103.16164/) (2021)
- [POSO: Personalized Cold Start Modules for Large-scale Recommender Systems](https://mlsanity.com/paper/2108.04690/) (2021)
- [Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems](https://mlsanity.com/paper/2405.13238/) (2024)

## Built on by (most cited first, 1 of 1)

- [ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling](https://mlsanity.com/paper/2606.24605/) (2026)

## Abstract (arXiv)

Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems. Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a systemic training bias toward high-activity users that overshadows the needs of low-activity users. To address these challenges, we propose ChoirRec, a novel framework that leverages the semantic capabilities of Large Language Models (LLMs) to construct semantic user groups and enhance CVR prediction for low-activity users. With a dual-channel architecture designed for robust cross-user knowledge transfer, ChoirRec comprises three components: (i) a Semantic Group Generation module that utilizes LLMs to form reliable, cross-activity user clusters, thereby filtering out noisy signals; (ii) a Group-aware Hierarchical Representation module that enriches sparse user embeddings with informative group-level priors to mitigate data insufficiency; and (iii) a Group-aware Multi-granularity Modual that employs a dual-channel architecture and adaptive fusion mechanism to ensure effective learning and utilization of group knowledge. We conduct extensive offline and online experiments on Taobao, a leading industrial-scale e-commerce platform. ChoirRec improves GAUC by 1.16% in offline evaluations, while online A/B testing reveals a 7.24% increase in order volume, highlighting its substantial practical value in real-world applications.