@inproceedings{ni-etal-2026-reasoning-based,
title = "Reasoning-Based Personalized Generation for Sparse Users",
author = "Ni, Bo and
Kveton, Branislav and
Basu, Samyadeep and
Mukherjee, Subhojyoti and
Wang, Leyao and
Dernoncourt, Franck and
Kim, Sungchul and
Yoon, Seunghyun and
Wang, Zichao and
Zhang, Ruiyi and
Mathur, Puneet and
Kil, Jihyung and
Gu, Jiuxiang and
Lipka, Nedim and
Wang, Yu and
Rossi, Ryan A. and
Derr, Tyler",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.219/",
pages = "4926--4926",
ISBN = "979-8-89176-439-2",
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."
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<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>
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%0 Conference Proceedings
%T Reasoning-Based Personalized Generation for Sparse Users
%A Ni, Bo
%A Kveton, Branislav
%A Basu, Samyadeep
%A Mukherjee, Subhojyoti
%A Wang, Leyao
%A Dernoncourt, Franck
%A Kim, Sungchul
%A Yoon, Seunghyun
%A Wang, Zichao
%A Zhang, Ruiyi
%A Mathur, Puneet
%A Kil, Jihyung
%A Gu, Jiuxiang
%A Lipka, Nedim
%A Wang, Yu
%A Rossi, Ryan A.
%A Derr, Tyler
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F ni-etal-2026-reasoning-based
%X 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.
%U https://aclanthology.org/2026.aacl-long.219/
%P 4926-4926
Markdown (Informal)
[Reasoning-Based Personalized Generation for Sparse Users](https://aclanthology.org/2026.aacl-long.219/) (Ni et al., AACL-IJCNLP 2026)
ACL
- 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, and Tyler Derr. 2026. Reasoning-Based Personalized Generation for Sparse Users. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4926–4926, Hengqin, China. Association for Computational Linguistics.