<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2017-handling">
    <titleInfo>
        <title>Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xuepeng</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kang</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jun</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Regina</namePart>
            <namePart type="family">Barzilay</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
            <namePart type="family">Kan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large scale dataset in an unsupervised way.</abstract>
    <identifier type="citekey">wang-etal-2017-handling</identifier>
    <identifier type="doi">10.18653/v1/P17-1034</identifier>
    <location>
        <url>https://aclanthology.org/P17-1034</url>
    </location>
    <part>
        <date>2017-07</date>
        <extent unit="page">
            <start>366</start>
            <end>376</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
%A Wang, Xuepeng
%A Liu, Kang
%A Zhao, Jun
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-etal-2017-handling
%X Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large scale dataset in an unsupervised way.
%R 10.18653/v1/P17-1034
%U https://aclanthology.org/P17-1034
%U https://doi.org/10.18653/v1/P17-1034
%P 366-376

Markdown (Informal)

[Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors](https://aclanthology.org/P17-1034) (Wang et al., ACL 2017)

• Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors (Wang et al., ACL 2017)

ACL

• Xuepeng Wang, Kang Liu, and Jun Zhao. 2017. Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 366–376, Vancouver, Canada. Association for Computational Linguistics.