Reasoning-Based Personalized Generation for Users with Sparse Data

[2602.21219] Reasoning-Based Personalized Generation for Users with Sparse Data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2602.21219 (cs) [Submitted on 31 Jan 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Reasoning-Based Personalized Generation for Users with Sparse Data Authors: Bo Ni , Branislav Kveton , Samyadeep Basu , Subhojyoti Mukherjee , Leyao Wang , Franck Dernoncourt , Sungchul Kim , Seunghyun Yoon , Zichao Wang , Ruiyi Zhang , Puneet Mathur , Jihyung Kil , Jiuxiang Gu , Nedim Lipka , Yu Wang , Ryan A. Rossi , Tyler Derr View a PDF of the paper titled Reasoning-Based Personalized Generation for Users with Sparse Data, by Bo Ni and 16 other authors 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. Extensive experiments on three benchmark personalized generation datasets show that GraSPer achieves significant performance gain, substantially improving personalization in sparse user context settings. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.21219 [cs.CL] (or arXiv:2602.21219v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.21219 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bo Ni [ view email ] [v1] Sat, 31 Jan 2026 01:54:23 UTC (1,141 KB) [v2] Fri, 14 Aug 2026 21:05:05 UTC (1,141 KB) Full-text links: Access Paper: View a PDF of the paper titled Reasoning-Based Personalized Generation for Users with Sparse Data, by Bo Ni and 16 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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