<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xing-paul-2017-incorporating">
    <titleInfo>
        <title>Incorporating Metadata into Content-Based User Embeddings</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Linzi</namePart>
        <namePart type="family">Xing</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="given">J</namePart>
        <namePart type="family">Paul</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 3rd Workshop on Noisy User-generated Text</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tim</namePart>
            <namePart type="family">Baldwin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Copenhagen, Denmark</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference. Recent work has shown that user embeddings can be improved by combining different types of information, such as text and network data. We propose a data augmentation method that allows novel feature types to be used within off-the-shelf embedding models. Experimenting with the task of friend recommendation on a dataset of 5,019 Twitter users, we show that our approach can lead to substantial performance gains with the simple addition of network and geographic features.</abstract>
    <identifier type="citekey">xing-paul-2017-incorporating</identifier>
    <identifier type="doi">10.18653/v1/W17-4406</identifier>
    <location>
        <url>https://aclanthology.org/W17-4406</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>45</start>
            <end>49</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Incorporating Metadata into Content-Based User Embeddings
%A Xing, Linzi
%A Paul, Michael J.
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F xing-paul-2017-incorporating
%X Low-dimensional vector representations of social media users can benefit applications like recommendation systems and user attribute inference. Recent work has shown that user embeddings can be improved by combining different types of information, such as text and network data. We propose a data augmentation method that allows novel feature types to be used within off-the-shelf embedding models. Experimenting with the task of friend recommendation on a dataset of 5,019 Twitter users, we show that our approach can lead to substantial performance gains with the simple addition of network and geographic features.
%R 10.18653/v1/W17-4406
%U https://aclanthology.org/W17-4406
%U https://doi.org/10.18653/v1/W17-4406
%P 45-49

Markdown (Informal)

[Incorporating Metadata into Content-Based User Embeddings](https://aclanthology.org/W17-4406) (Xing & Paul, WNUT 2017)

• Incorporating Metadata into Content-Based User Embeddings (Xing & Paul, WNUT 2017)

ACL

• Linzi Xing and Michael J. Paul. 2017. Incorporating Metadata into Content-Based User Embeddings. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 45–49, Copenhagen, Denmark. Association for Computational Linguistics.