Enhanced Whole Page Optimization via Mixed-Grained Reward Mechanism-Adapted Language Models

• URL: http://arxiv.org/abs/2506.09084v1
• Date: Tue, 10 Jun 2025 08:05:42 GMT
• Title: Enhanced Whole Page Optimization via Mixed-Grained Reward Mechanism-Adapted Language Models
• Authors: Xinyuan Wang, Liang Wu, Yanjie Fu,
• Abstract summary: We propose a reward-based fine-tuning approach, which employs a mixed-grained reward mechanism that combines page-level and item-level rewards.<n>PageLLM outperforms baselines and achieves a 0.44% GMV increase in an online A/B test with over 10 million users.
• Score: 21.43957709683293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimizing the presentation of search and recommendation results is crucial to enhancing user experience and engagement. Whole Page Optimization (WPO) plays a pivotal role in this process, as it directly influences how information is surfaced to users. While Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities in generating coherent and contextually relevant content, fine-tuning these models for complex tasks like WPO presents challenges. Specifically, the need for extensive human-annotated data to mitigate issues such as hallucinations and model instability can be prohibitively expensive, especially in large-scale systems that interact with millions of items daily. In this work, we address the challenge of fine-tuning LLMs for WPO by using user feedback as the supervision. Unlike manually labeled datasets, user feedback is inherently noisy and less precise. To overcome this, we propose a reward-based fine-tuning approach, PageLLM, which employs a mixed-grained reward mechanism that combines page-level and item-level rewards. The page-level reward evaluates the overall quality and coherence, while the item-level reward focuses on the accuracy and relevance of key recommendations. This dual-reward structure ensures that both the holistic presentation and the critical individual components are optimized. We validate PageLLM on both public and industrial datasets. PageLLM outperforms baselines and achieves a 0.44\% GMV increase in an online A/B test with over 10 million users, demonstrating its real-world impact.

Related papers

• End-to-End Personalization: Unifying Recommender Systems with Large Language Models [0.0]
  We propose a novel hybrid recommendation framework that combines Graph Attention Networks (GATs) with Large Language Models (LLMs)<n>LLMs are first used to enrich user and item representations by generating semantically meaningful profiles based on metadata such as titles, genres, and overviews.<n>We evaluate our model on benchmark datasets, including MovieLens 100k and 1M, where it consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-08-02T22:46:50Z)
• RecGPT Technical Report [57.84251629878726]
  We propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline.<n> RecGPT integrates large language models into key stages of user interest mining, item retrieval, and explanation generation.<n>Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders.
  arXiv  Detail & Related papers  (2025-07-30T17:55:06Z)
• 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)
• Reasoning LLMs for User-Aware Multimodal Conversational Agents [3.533721662684487]
  Personalization in social robotics is critical for fostering effective human-robot interactions.<n>This paper proposes a novel framework called USER-LLM R1 for a user-aware conversational agent.<n>Our approach integrates chain-of-thought (CoT) reasoning models to iteratively infer user preferences and vision-language models.
  arXiv  Detail & Related papers  (2025-04-02T13:00:17Z)
• FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users [111.56469697145519]
  We propose Few-Shot Preference Optimization, which reframes reward modeling as a meta-learning problem.<n>Under this framework, an LLM learns to quickly adapt to a user via a few labeled preferences from that user, constructing a personalized reward function for them.<n>We generate over 1M synthetic personalized preferences using publicly available LLMs.<n>We evaluate FSPO on personalized open-ended generation for up to 1,500 synthetic users across three domains: movie reviews, pedagogical adaptation based on educational background, and general question answering, along with a controlled human study.
  arXiv  Detail & Related papers  (2025-02-26T17:08:46Z)
• Systematic Reward Gap Optimization for Mitigating VLM Hallucinations [34.71750379630014]
  We introduce Topic-level Preference Rewriting (TPR), a novel framework designed for the systematic optimization of reward gap configuration.<n>TPR provides topic-level control over fine-grained semantic details, enabling advanced data curation strategies.<n>It significantly reduces hallucinations by up to 93% on ObjectHal-Bench, and also exhibits superior data efficiency towards robust and cost-effective VLM alignment.
  arXiv  Detail & Related papers  (2024-11-26T09:42:07Z)
• Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback [41.88662700261036]
  RAG systems face limitations when semantic relevance alone does not guarantee improved generation quality. We propose Pistis-RAG, a new RAG framework designed with a content-centric approach to better align LLMs with human preferences.
  arXiv  Detail & Related papers  (2024-06-21T08:52:11Z)
• Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement [79.2400720115588]
  We introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts.<n>In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size.<n>Our experiments also indicate a marked improvement of over 10% under cold-start scenarios, when users have extremely sparse data.
  arXiv  Detail & Related papers  (2024-02-16T20:20:43Z)
• InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
  Reward hacking, also termed reward overoptimization, remains a critical challenge. We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective. We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
  arXiv  Detail & Related papers  (2024-02-14T17:49:07Z)
• Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications [0.7832189413179361]
  Large Language Models (LLMs) excel in comprehending and generating human-like text. This paper explores strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems.
  arXiv  Detail & Related papers  (2023-11-21T02:01:01Z)
• Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning [65.51668094117802]
  We propose a human-centered interactive HPO approach tailored towards multi-objective machine learning (ML) Instead of relying on the user guessing the most suitable indicator for their needs, our approach automatically learns an appropriate indicator.
  arXiv  Detail & Related papers  (2023-09-07T09:22:05Z)
• ICPE: An Item Cluster-Wise Pareto-Efficient Framework for Recommendation Debiasing [7.100121083949393]
  In this work, we explore the central theme of recommendation debiasing from an item cluster-wise multi-objective optimization perspective.<n>Aiming to balance the learning on various item clusters that differ in popularity during the training process, we propose a model-agnostic framework namely Item Cluster-Wise.<n>In detail, we define our item cluster-wise optimization target as the recommender model should balance all item clusters that differ in popularity.
  arXiv  Detail & Related papers  (2021-09-27T09:17:53Z)

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.