LaMP-Val: Large Language Models Empower Personalized Valuation in Auction

TL;DR

This paper introduces LaMP-Val, a framework using Large Language Models to incorporate individual user preferences into auction valuation, improving accuracy and profit in personalized bidding scenarios.

Contribution

It presents a novel LLM-based personalized valuation framework with data, learning, and evaluation components, addressing a gap in auction research.

Findings

LaMP-Val more accurately captures personalized user values.

It achieves higher profits compared to baseline methods.

The framework effectively models fine-grained valuation patterns.

Abstract

Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users' unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large \underline{La}nguage \underline{M}odels-powered \underline{P}ersonalized \underline{Val}uation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs' capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.