High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning

2026-02-07

AI Summary

This work addresses the challenge of unified representation learning from multi-source heterogeneous user texts—such as profiles, job histories, and search logs—in large-scale job platforms. We propose a novel unsupervised framework that leverages reinforcement learning to fuse these diverse textual sources into concise, interpretable user representations tailored for large language models (LLMs). The approach utilizes implicit user interaction signals (e.g., clicks and applications) as the primary reward, augmented with rule-based rewards to constrain output format and length, thereby achieving both business relevance and interpretability without manual annotations. Extensive offline experiments across multiple LinkedIn product lines demonstrate significant improvements in key downstream recommendation metrics, validating the method’s effectiveness, scalability, and practical utility.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsData Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Effective personalization on large-scale job platforms requires modeling members based on heterogeneous textual sources, including profiles, professional data, and search activity logs. As recommender systems increasingly adopt Large Language Models (LLMs), creating unified, interpretable, and concise representations from heterogeneous sources becomes critical, especially for latency-sensitive online environments. In this work, we propose a novel Reinforcement Learning (RL) framework to synthesize a unified textual representation for each member. Our approach leverages implicit user engagement signals (e.g., clicks, applies) as the primary reward to distill salient information. Additionally, the framework is complemented by rule-based rewards that enforce formatting and length constraints. Extensive offline experiments across multiple LinkedIn products, one of the world's largest job platforms, demonstrate significant improvements in key downstream business metrics. This work provides a practical, labeling-free, and scalable solution for constructing interpretable user representations that are directly compatible with LLM-based systems.

Problem

• user representation
• heterogeneous sources
• large language models
• personalization
• job platforms

Innovation

• Reinforcement Learning
• User Representation
• Heterogeneous Text Sources
• Large Language Models
• Interpretable Representation