基于推理的稀疏数据用户个性化生成
Reasoning-Based Personalized Generation for Users with Sparse Data
AI总结：
GraSPer通过预测用户未来交互并生成文本来增强稀疏上下文中的个性化生成，提升用户个性化输出质量。
摘要：
大型语言模型（LLM）个性化在利用个人上下文和历史来定制响应方面具有巨大潜力。然而，现实中的用户通常拥有稀疏的交互历史，具有有限的个人上下文，例如社交平台上的冷启动用户和电子商务平台的新注册客户，这损害了基于LLM的个性化生成。为了解决这一挑战，我们引入了GraSPer（基于图的稀疏个性化推理），一种新的框架，用于在稀疏上下文中增强个性化文本生成。GraSPer首先通过预测用户未来可能交互的项目来增强用户上下文。然后，通过推理对齐，生成这些交互的文本以丰富增强的上下文。最后，它基于真实和合成历史生成个性化输出，确保与用户风格和偏好的对齐。在三个基准个性化生成数据集上的广泛实验表明，GraSPer实现了显著的性能提升，大幅提高了稀疏用户上下文设置中的个性化水平。
英文摘要：
Large Language Model (LLM) personalization holds great promise for tailoring responses by leveraging personal context and history. However, real-world users usually possess sparse interaction histories with limited personal context, such as cold-start users in social platforms and newly registered customers in online E-commerce platforms, compromising the LLM-based personalized generation. To address this challenge, we introduce GraSPer (Graph-based Sparse Personalized Reasoning), a novel framework for enhancing personalized text generation under sparse context. GraSPer first augments user context by predicting items that the user would likely interact with in the future. With reasoning alignment, it then generates texts for these interactions to enrich the augmented context. In the end, it generates personalized outputs conditioned on both the real and synthetic histories, ensuring alignment with user style and preferences. Extensive experiments on three benchmark personalized generation datasets show that GraSPer achieves significant performance gain, substantially improving personalization in sparse user context settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
