@inproceedings{choi-etal-2026-align,
title = "Align, Don{'}t Fuse: Structure-Preserving Alignment of Collaborative and Semantic Signals for {LLM}-Enhanced Recommendation",
author = "Choi, Jooweon and
Kim, MiHyeon and
Lee, Eunju and
Kwon, Junehyoung and
Kim, YoungBin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.356/",
pages = "7444--7479",
ISBN = "979-8-89176-462-0",
abstract = "Large Language Models (LLMs) provide rich semantic representations for recommender systems, but their semantic similarity may not align with collaborative preference patterns captured by collaborative filtering (CF). Directly combining LLM and CF representations can therefore distort the structural relationships encoded in the interaction graph. We propose Structure-Preserving Alignment (SPA), which reformulates CF{--}LLM integration as language-supervised representation alignment. SPA derives collaborative anchors from frozen CF representations and aligns multiple LLM-generated semantic views toward these anchors through cross-profile view competition. The aligned collaborative representations are then used for structure-preserving two-channel retrieval, which constructs behavior-aware context for downstream LLM-based preference prediction. Experiments on multiple datasets and CF backbones show that SPA consistently improves click-through rate (CTR)-style binary preference prediction."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="choi-etal-2026-align">
<titleInfo>
<title>Align, Don’t Fuse: Structure-Preserving Alignment of Collaborative and Semantic Signals for LLM-Enhanced Recommendation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jooweon</namePart>
<namePart type="family">Choi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">MiHyeon</namePart>
<namePart type="family">Kim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eunju</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Junehyoung</namePart>
<namePart type="family">Kwon</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">YoungBin</namePart>
<namePart type="family">Kim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>Large Language Models (LLMs) provide rich semantic representations for recommender systems, but their semantic similarity may not align with collaborative preference patterns captured by collaborative filtering (CF). Directly combining LLM and CF representations can therefore distort the structural relationships encoded in the interaction graph. We propose Structure-Preserving Alignment (SPA), which reformulates CF–LLM integration as language-supervised representation alignment. SPA derives collaborative anchors from frozen CF representations and aligns multiple LLM-generated semantic views toward these anchors through cross-profile view competition. The aligned collaborative representations are then used for structure-preserving two-channel retrieval, which constructs behavior-aware context for downstream LLM-based preference prediction. Experiments on multiple datasets and CF backbones show that SPA consistently improves click-through rate (CTR)-style binary preference prediction.</abstract>
<identifier type="citekey">choi-etal-2026-align</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.356/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>7444</start>
<end>7479</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Align, Don’t Fuse: Structure-Preserving Alignment of Collaborative and Semantic Signals for LLM-Enhanced Recommendation
%A Choi, Jooweon
%A Kim, MiHyeon
%A Lee, Eunju
%A Kwon, Junehyoung
%A Kim, YoungBin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F choi-etal-2026-align
%X Large Language Models (LLMs) provide rich semantic representations for recommender systems, but their semantic similarity may not align with collaborative preference patterns captured by collaborative filtering (CF). Directly combining LLM and CF representations can therefore distort the structural relationships encoded in the interaction graph. We propose Structure-Preserving Alignment (SPA), which reformulates CF–LLM integration as language-supervised representation alignment. SPA derives collaborative anchors from frozen CF representations and aligns multiple LLM-generated semantic views toward these anchors through cross-profile view competition. The aligned collaborative representations are then used for structure-preserving two-channel retrieval, which constructs behavior-aware context for downstream LLM-based preference prediction. Experiments on multiple datasets and CF backbones show that SPA consistently improves click-through rate (CTR)-style binary preference prediction.
%U https://aclanthology.org/2026.findings-emnlp.356/
%P 7444-7479
Markdown (Informal)
[Align, Don’t Fuse: Structure-Preserving Alignment of Collaborative and Semantic Signals for LLM-Enhanced Recommendation](https://aclanthology.org/2026.findings-emnlp.356/) (Choi et al., Findings 2026)
ACL