• Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:Reasoning-Based Personalized Generation for Users with Sparse Data View PDF HTML (experimental)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

Article Summaries:

  • Computer Science > Computation and Language [Submitted on 31 Jan 2026] Title:Reasoning-Based Personalized Generation for Users with Sparse Data View PDF HTML (experimental)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,

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