<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="na-etal-2024-enhancing">
    <titleInfo>
        <title>Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hyunsoo</namePart>
        <namePart type="family">Na</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Minseok</namePart>
        <namePart type="family">Gang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Youngrok</namePart>
        <namePart type="family">Ko</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jinseok</namePart>
        <namePart type="family">Seol</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sang-goo</namePart>
        <namePart type="family">Lee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Large language models (LLMs) are utilized in various studies, and they also demonstrate a potential to function independently as a recommendation model. Nevertheless, training sequences and text labels modifies LLMs’ pre-trained weights, diminishing their inherent strength in constructing and comprehending natural language sentences. In this study, we propose a reconstruction-based LLM recommendation model (ReLRec) that harnesses the feature extraction capability of LLMs, while preserving LLMs’ sentence generation abilities. We reconstruct the user and item pseudo-labels generated from user reviews, while training on sequential data, aiming to exploit the key features of both users and items. Experimental results demonstrate the efficacy of label reconstruction in sequential recommendation tasks.</abstract>
    <identifier type="citekey">na-etal-2024-enhancing</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-emnlp.423</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>7213</start>
            <end>7222</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction
%A Na, Hyunsoo
%A Gang, Minseok
%A Ko, Youngrok
%A Seol, Jinseok
%A Lee, Sang-goo
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F na-etal-2024-enhancing
%X Large language models (LLMs) are utilized in various studies, and they also demonstrate a potential to function independently as a recommendation model. Nevertheless, training sequences and text labels modifies LLMs’ pre-trained weights, diminishing their inherent strength in constructing and comprehending natural language sentences. In this study, we propose a reconstruction-based LLM recommendation model (ReLRec) that harnesses the feature extraction capability of LLMs, while preserving LLMs’ sentence generation abilities. We reconstruct the user and item pseudo-labels generated from user reviews, while training on sequential data, aiming to exploit the key features of both users and items. Experimental results demonstrate the efficacy of label reconstruction in sequential recommendation tasks.
%U https://aclanthology.org/2024.findings-emnlp.423
%P 7213-7222

Markdown (Informal)

[Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction](https://aclanthology.org/2024.findings-emnlp.423) (Na et al., Findings 2024)

• Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction (Na et al., Findings 2024)

ACL

• Hyunsoo Na, Minseok Gang, Youngrok Ko, Jinseok Seol, and Sang-goo Lee. 2024. Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7213–7222, Miami, Florida, USA. Association for Computational Linguistics.