PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence

• URL: http://arxiv.org/abs/2503.02398v2
• Date: Sat, 24 May 2025 04:37:34 GMT
• Title: PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence
• Authors: Yunxiao Shi, Wujiang Xu, Zeqi Zhang, Xing Zi, Qiang Wu, Min Xu,
• Abstract summary: PersonaX is an agent-agnostic LLM-UM framework to address these challenges.<n>It augments downstream recommendation agents to achieve better recommendation performance and inference efficiency.
• Score: 8.557534799079956
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process. High-quality user profiles are essential for aligning agent behavior with real user interests. Typically, these profiles are constructed by leveraging LLMs for user profile modeling (LLM-UM). However, this process faces several challenges: (1) LLMs struggle with long user behaviors due to context length limitations and performance degradation. (2) Existing methods often extract only partial segments from full historical behavior sequence, inevitably discarding diverse user interests embedded in the omitted content, leading to incomplete modeling and suboptimal profiling. (3) User profiling is often tightly coupled with the inference context, requiring online processing, which introduces significant latency overhead. In this paper, we propose PersonaX, an agent-agnostic LLM-UM framework to address these challenges. It augments downstream recommendation agents to achieve better recommendation performance and inference efficiency. PersonaX (a) segments complete historical behaviors into clustered groups, (b) selects multiple sub behavior sequences (SBS) with a balance of prototypicality and diversity to form a high quality core set, (c) performs offline multi-persona profiling to capture diverse user interests and generate fine grained, cached textual personas, and (d) decouples user profiling from online inference, enabling profile retrieval instead of real time generation. Extensive experiments demonstrate its effectiveness: using only 30 to 50% of behavioral data (sequence length 480), PersonaX enhances AgentCF by 3 to 11% and Agent4Rec by 10 to 50%. As a scalable and model-agnostic LLM-UM solution, PersonaX sets a new benchmark in scalable user modeling.

Related papers

• LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation [12.89199121698673]
  Large language models (LLMs) show significant potential for multi-interest analysis due to their extensive knowledge and powerful reasoning capabilities.<n>We propose an LLM-driven dual-level multi-interest modeling framework for more effective recommendation.<n> Experiments on real-world datasets show the superiority of our approach against state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-15T02:13:54Z)
• LettinGo: Explore User Profile Generation for Recommendation System [45.40232561275015]
  We introduce LettinGo, a novel framework for generating diverse and adaptive user profiles.<n>Our framework significantly enhances recommendation accuracy, flexibility, and contextual awareness.
  arXiv  Detail & Related papers  (2025-06-23T05:51:52Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [51.9706400130481]
  Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.<n> PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.<n>We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
  arXiv  Detail & Related papers  (2025-04-19T08:16:10Z)
• AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models [10.52052172996229]
  AdaptRec is a self-adaptive fram-ework that leverages Large Language Models for sequential recommendations by incorporating explicit collaborative signals. We develop a User-Contextualized Recommendation Prompt that translates their behavior sequences into natural language, explicitly integrating this information into the recommendation process. Experiments demonstrate AdaptRec's superior performance, with significant improvements in HitRatio@1 scores of 7.13%, 18.16%, and 10.41% across real-world datasets.
  arXiv  Detail & Related papers  (2025-04-06T00:30:50Z)
• Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations [12.814937243361054]
  We propose a hybrid task allocation framework designed to promote social good by equitably serving all user groups.<n>Our results on three real-world datasets show a significant reduction in weak users and improved robustness to subpopulations without disproportionately escalating costs.
  arXiv  Detail & Related papers  (2025-01-08T18:08:48Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications. Ensuring their alignment with the diverse preferences of individual users has become a critical challenge. We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• LIBER: Lifelong User Behavior Modeling Based on Large Language Models [42.045535303737694]
  We propose Lifelong User Behavior Modeling (LIBER) based on large language models. LIBER has been deployed on Huawei's music recommendation service and achieved substantial improvements in users' play count and play time by 3.01% and 7.69%.
  arXiv  Detail & Related papers  (2024-11-22T03:43:41Z)
• Improved Diversity-Promoting Collaborative Metric Learning for Recommendation [127.08043409083687]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2024-09-02T07:44:48Z)
• Prompt Tuning as User Inherent Profile Inference Machine [53.78398656789463]
  We propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. A profile quantization codebook bridges the modality gap by profile embeddings into collaborative IDs. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms.
  arXiv  Detail & Related papers  (2024-08-13T02:25:46Z)
• Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
  We focus on the field of large language models (LLMs) for recommendation. We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users. We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
  arXiv  Detail & Related papers  (2024-08-07T04:20:28Z)
• Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment [104.18002641195442]
  We introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training paradigm that does not require existing paired data. Building on the self-play concept, which autonomously generates negative responses, we further incorporate an off-policy learning pipeline to enhance data exploration and exploitation.
  arXiv  Detail & Related papers  (2024-05-31T14:21:04Z)
• Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations [11.004673022505566]
  Long user queries from millions of users can degrade the performance of large language models for recommendation. We propose a hybrid task allocation framework that utilizes the capabilities of both large language models and traditional recommendation systems. Our results on three real-world datasets show a significant reduction in weak users and improved robustness of RSs to sub-populations.
  arXiv  Detail & Related papers  (2024-05-01T19:11:47Z)
• Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning [36.88126051792774]
  Personalization in large language models (LLMs) is increasingly important.<n>One PEFT Per User (OPPU) employs personalized parameter-efficient fine-tuning (PEFT) modules to store user-specific behavior patterns and preferences.<n>OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark.
  arXiv  Detail & Related papers  (2024-02-06T21:03:52Z)
• Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models [11.950478880423733]
  Personalization is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. We propose a novel summary-augmented personalization with task-aware user summaries generated by LLMs.
  arXiv  Detail & Related papers  (2023-10-30T23:40:41Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm [154.47590401735323]
  Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems. This paper focuses on a challenging scenario where a user has multiple categories of interests. We propose a novel method called textitDiversity-Promoting Collaborative Metric Learning (DPCML)
  arXiv  Detail & Related papers  (2022-09-30T08:02:18Z)

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.