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

• URL: http://arxiv.org/abs/2510.09393v2
• Date: Mon, 13 Oct 2025 02:54:57 GMT
• Title: ChoirRec: Semantic User Grouping via LLMs for Conversion Rate Prediction of Low-Activity Users
• Abstract summary: ChoirRec is a novel framework that leverages the semantic capabilities of Large Language Models (LLMs) to construct semantic user groups.<n>With a dual-channel architecture designed for robust cross-user knowledge transfer, ChoirRec comprises three components.<n>We conduct extensive offline and online experiments on Taobao, a leading industrial-scale e-commerce platform.
• Score: 9.307320121599828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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