<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2025-incorporating">
    <titleInfo>
        <title>Incorporating Review-missing Interactions for Generative Explainable Recommendation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xi</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiaohe</namePart>
        <namePart type="family">Bo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chen</namePart>
        <namePart type="family">Ma</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xu</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-01</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 31st International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Owen</namePart>
            <namePart type="family">Rambow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leo</namePart>
            <namePart type="family">Wanner</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hend</namePart>
            <namePart type="family">Al-Khalifa</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Barbara</namePart>
            <namePart type="given">Di</namePart>
            <namePart type="family">Eugenio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Schockaert</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, UAE</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Explainable recommendation has attracted much attention from the academic and industry communities. Traditional models usually leverage user reviews as ground truths for model training, and the interactions without reviews are totally ignored. However, in practice, a large amount of users may not leave reviews after purchasing items. In this paper, we argue that the interactions without reviews may also contain comprehensive user preferences, and incorporating them to build explainable recommender model may further improve the explanation quality. To follow such intuition, we first leverage generative models to predict the missing reviews, and then train the recommender model based on all the predicted and original reviews. In specific, since the reviews are discrete tokens, we regard the review generation process as a reinforcement learning problem, where each token is an action at one step. We hope that the generated reviews are indistinguishable with the real ones. Thus, we introduce an discriminator as a reward model to evaluate the quality of the generated reviews. At last, to smooth the review generation process, we introduce a self-paced learning strategy to first generate shorter reviews and then predict the longer ones. We conduct extensive experiments on three publicly available datasets to demonstrate the effectiveness of our model.</abstract>
    <identifier type="citekey">li-etal-2025-incorporating</identifier>
    <location>
        <url>https://aclanthology.org/2025.coling-main.527/</url>
    </location>
    <part>
        <date>2025-01</date>
        <extent unit="page">
            <start>7870</start>
            <end>7880</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Incorporating Review-missing Interactions for Generative Explainable Recommendation
%A Li, Xi
%A Bo, Xiaohe
%A Ma, Chen
%A Chen, Xu
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F li-etal-2025-incorporating
%X Explainable recommendation has attracted much attention from the academic and industry communities. Traditional models usually leverage user reviews as ground truths for model training, and the interactions without reviews are totally ignored. However, in practice, a large amount of users may not leave reviews after purchasing items. In this paper, we argue that the interactions without reviews may also contain comprehensive user preferences, and incorporating them to build explainable recommender model may further improve the explanation quality. To follow such intuition, we first leverage generative models to predict the missing reviews, and then train the recommender model based on all the predicted and original reviews. In specific, since the reviews are discrete tokens, we regard the review generation process as a reinforcement learning problem, where each token is an action at one step. We hope that the generated reviews are indistinguishable with the real ones. Thus, we introduce an discriminator as a reward model to evaluate the quality of the generated reviews. At last, to smooth the review generation process, we introduce a self-paced learning strategy to first generate shorter reviews and then predict the longer ones. We conduct extensive experiments on three publicly available datasets to demonstrate the effectiveness of our model.
%U https://aclanthology.org/2025.coling-main.527/
%P 7870-7880

Markdown (Informal)

[Incorporating Review-missing Interactions for Generative Explainable Recommendation](https://aclanthology.org/2025.coling-main.527/) (Li et al., COLING 2025)

• Incorporating Review-missing Interactions for Generative Explainable Recommendation (Li et al., COLING 2025)

ACL

• Xi Li, Xiaohe Bo, Chen Ma, and Xu Chen. 2025. Incorporating Review-missing Interactions for Generative Explainable Recommendation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 7870–7880, Abu Dhabi, UAE. Association for Computational Linguistics.