High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning

• URL: http://arxiv.org/abs/2602.07333v1
• Date: Sat, 07 Feb 2026 03:27:55 GMT
• Title: High Fidelity Textual User Representation over Heterogeneous Sources via Reinforcement Learning
• Authors: Rajat Arora, Ye Tao, Jianqiang Shen, Ping Liu, Muchen Wu, Qianqi Shen, Benjamin Le, Fedor Borisyuk, Jingwei Wu, Wenjing Zhang,
• Abstract summary: We propose a novel Reinforcement Learning (RL) framework to synthesize a unified textual representation for each member.<n>Our approach leverages implicit user engagement signals (e.g., clicks, applies) as the primary reward to distill salient information.<n>This work provides a practical, labeling-free, and scalable solution for constructing interpretable user representations.
• Score: 7.091225956788446
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Large Language Model Prompt Datasets: An In-depth Analysis and Insights [17.386420251846953]
  A prompt is a natural language instruction that defines a specific task for a large language model (LLM)<n>In this work, we--for the first time--have compiled an extensive list of prompt datasets sourced from various channels.
  arXiv  Detail & Related papers  (2025-10-10T12:15:55Z)
• From What to Why: Thought-Space Recommendation with Small Language Models [2.134948383299948]
  Large Language Models (LLMs) have advanced recommendation capabilities through enhanced reasoning, but pose significant challenges for real-world deployment.<n>Small Language Models (SLMs) offer an efficient alternative, their reasoning capabilities for recommendation remain underexplored.<n>We propose PULSE (Preference Understanding by Latent Semantic Embeddings), a framework that treats SLM-generated rationales as director learning signals.
  arXiv  Detail & Related papers  (2025-10-08T11:22:26Z)
• From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling [3.304341919932024]
  We introduce a novel large language model (LLM)-based approach that leverages domain-defining statements.<n>Our method generates interpretable natural language user profiles, condensing extensive user data into a scale.<n> Experimental results show our method significantly outperforms state-of-the-art LLM-based and traditional methods by 9.8%.
  arXiv  Detail & Related papers  (2025-05-09T16:51:24Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Language Representations Can be What Recommenders Need: Findings and Potentials [57.90679739598295]
  We show that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance.<n>This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation.<n>Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
  arXiv  Detail & Related papers  (2024-07-07T17:05:24Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Representation Learning with Large Language Models for Recommendation [33.040389989173825]
  We propose a model-agnostic framework RLMRec to enhance recommenders with large language models (LLMs)empowered representation learning.<n>RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals.
  arXiv  Detail & Related papers  (2023-10-24T15:51:13Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Offline RL for Natural Language Generation with Implicit Language Q Learning [87.76695816348027]
  Large language models can be inconsistent when it comes to completing user specified tasks. We propose a novel RL method, that combines both the flexible utility framework of RL with the ability of supervised learning. In addition to empirically validating ILQL, we present a detailed empirical analysis situations where offline RL can be useful in natural language generation settings.
  arXiv  Detail & Related papers  (2022-06-05T18:38:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.