@inproceedings{park-kim-2026-grounding,
title = "Grounding What Users Care About: Multi-Aspect Personalized Modeling from Implicit Preferences",
author = "Park, Jiin and
Kim, Misuk",
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.95/",
pages = "2063--2089",
ISBN = "979-8-89176-439-2",
abstract = "Large Language Model (LLM) personalization is challenging because user preferences are rarely stated explicitly; instead, they emerge as implicit, multifaceted signals in users' past interaction histories. Existing methods often compress user histories into a single representation or retrieve and inject past records without explicitly linking them to supporting evidence, which can lead to hallucinated or overly generic outputs. In this work, we propose Multi-Aspect PersonaLized Modeling with Evidence (MAPLE). MAPLE structures implicit user preferences through a MULTI-ASPECT PREFERENCE PROFILE and connects them to target-specific grounding evidence through a PERSONALIZED EVIDENCE PLAN. By selecting only generation points that are both relevant to the user and supported by available evidence, MAPLE improves personalization and grounding faithfulness simultaneously. Experiments on the Games and Books domains with two backbone LLMs show that MAPLE consistently outperforms strong baselines and generalizes robustly under limited-history and unseen-user settings."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="park-kim-2026-grounding">
<titleInfo>
<title>Grounding What Users Care About: Multi-Aspect Personalized Modeling from Implicit Preferences</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jiin</namePart>
<namePart type="family">Park</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Misuk</namePart>
<namePart type="family">Kim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>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)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruifeng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayu</namePart>
<namePart type="family">Purwarianti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Rong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Suzuki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hengqin, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-439-2</identifier>
</relatedItem>
<abstract>Large Language Model (LLM) personalization is challenging because user preferences are rarely stated explicitly; instead, they emerge as implicit, multifaceted signals in users’ past interaction histories. Existing methods often compress user histories into a single representation or retrieve and inject past records without explicitly linking them to supporting evidence, which can lead to hallucinated or overly generic outputs. In this work, we propose Multi-Aspect PersonaLized Modeling with Evidence (MAPLE). MAPLE structures implicit user preferences through a MULTI-ASPECT PREFERENCE PROFILE and connects them to target-specific grounding evidence through a PERSONALIZED EVIDENCE PLAN. By selecting only generation points that are both relevant to the user and supported by available evidence, MAPLE improves personalization and grounding faithfulness simultaneously. Experiments on the Games and Books domains with two backbone LLMs show that MAPLE consistently outperforms strong baselines and generalizes robustly under limited-history and unseen-user settings.</abstract>
<identifier type="citekey">park-kim-2026-grounding</identifier>
<location>
<url>https://aclanthology.org/2026.aacl-long.95/</url>
</location>
<part>
<date>2026-11</date>
<extent unit="page">
<start>2063</start>
<end>2089</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Grounding What Users Care About: Multi-Aspect Personalized Modeling from Implicit Preferences
%A Park, Jiin
%A Kim, Misuk
%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 park-kim-2026-grounding
%X Large Language Model (LLM) personalization is challenging because user preferences are rarely stated explicitly; instead, they emerge as implicit, multifaceted signals in users’ past interaction histories. Existing methods often compress user histories into a single representation or retrieve and inject past records without explicitly linking them to supporting evidence, which can lead to hallucinated or overly generic outputs. In this work, we propose Multi-Aspect PersonaLized Modeling with Evidence (MAPLE). MAPLE structures implicit user preferences through a MULTI-ASPECT PREFERENCE PROFILE and connects them to target-specific grounding evidence through a PERSONALIZED EVIDENCE PLAN. By selecting only generation points that are both relevant to the user and supported by available evidence, MAPLE improves personalization and grounding faithfulness simultaneously. Experiments on the Games and Books domains with two backbone LLMs show that MAPLE consistently outperforms strong baselines and generalizes robustly under limited-history and unseen-user settings.
%U https://aclanthology.org/2026.aacl-long.95/
%P 2063-2089
Markdown (Informal)
[Grounding What Users Care About: Multi-Aspect Personalized Modeling from Implicit Preferences](https://aclanthology.org/2026.aacl-long.95/) (Park & Kim, AACL-IJCNLP 2026)
ACL