Abstract
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.
Abstract (translated)
大型语言模型（LLM）的个性化在利用个人背景和历史信息定制回复方面展现出巨大潜力。然而，现实世界中的用户通常只有稀疏的历史交互记录和个人背景资料，例如社交平台上的新注册用户或电子商务平台上刚注册的新客户，在这种情况下，基于LLM的个性化生成效果会受到限制。为了解决这一挑战，我们提出了一种新的框架GraSPer（基于图的稀疏个性化推理），旨在增强在稀疏上下文下的个性化文本生成。 GraSPer首先通过预测用户未来可能与之互动的项目来扩充用户的背景信息。然后，在进行推理对齐后，它为这些交互生成文本以丰富扩增后的背景资料。最后，基于真实和合成的历史记录条件，它生成个性化的输出，确保符合用户的风格和偏好。 在三个基准个性化生成数据集上进行了广泛的实验，结果显示GraSPer取得了显著的性能提升，在稀疏用户上下文的情况下大幅提高了个性化程度。
URL
https://arxiv.org/abs/2602.21219
