AspeRa: Aspect-Based Rating Prediction Based on User Reviews

Elena Tutubalina; Valentin Malykh; Sergey Nikolenko; Anton Alekseev; Ilya Shenbin

AspeRa: Aspect-Based Rating Prediction Based on User Reviews

Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)

Anthology ID:: W19-3605
Volume:: Proceedings of the 2019 Workshop on Widening NLP
Month:: August
Year:: 2019
Address:: Florence, Italy
Editors:: Amittai Axelrod, Diyi Yang, Rossana Cunha, Samira Shaikh, Zeerak Waseem
Venue:: WiNLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 11–13
Language:
URL:: https://aclanthology.org/W19-3605/
DOI:
Bibkey:
Cite (ACL):: Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, and Ilya Shenbin. 2019. AspeRa: Aspect-Based Rating Prediction Based on User Reviews. In Proceedings of the 2019 Workshop on Widening NLP, pages 11–13, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):: AspeRa: Aspect-Based Rating Prediction Based on User Reviews (Tutubalina et al., WiNLP 2019)
Copy Citation:

Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{tutubalina-etal-2019-aspera,
    title = "{A}spe{R}a: Aspect-Based Rating Prediction Based on User Reviews",
    author = "Tutubalina, Elena  and
      Malykh, Valentin  and
      Nikolenko, Sergey  and
      Alekseev, Anton  and
      Shenbin, Ilya",
    editor = "Axelrod, Amittai  and
      Yang, Diyi  and
      Cunha, Rossana  and
      Shaikh, Samira  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-3605/",
    pages = "11--13",
    abstract = "We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)"
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tutubalina-etal-2019-aspera">
    <titleInfo>
        <title>AspeRa: Aspect-Based Rating Prediction Based on User Reviews</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Elena</namePart>
        <namePart type="family">Tutubalina</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Valentin</namePart>
        <namePart type="family">Malykh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sergey</namePart>
        <namePart type="family">Nikolenko</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anton</namePart>
        <namePart type="family">Alekseev</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ilya</namePart>
        <namePart type="family">Shenbin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Workshop on Widening NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Amittai</namePart>
            <namePart type="family">Axelrod</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Diyi</namePart>
            <namePart type="family">Yang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rossana</namePart>
            <namePart type="family">Cunha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Samira</namePart>
            <namePart type="family">Shaikh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)</abstract>
    <identifier type="citekey">tutubalina-etal-2019-aspera</identifier>
    <location>
        <url>https://aclanthology.org/W19-3605/</url>
    </location>
    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>11</start>
            <end>13</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T AspeRa: Aspect-Based Rating Prediction Based on User Reviews
%A Tutubalina, Elena
%A Malykh, Valentin
%A Nikolenko, Sergey
%A Alekseev, Anton
%A Shenbin, Ilya
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F tutubalina-etal-2019-aspera
%X We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)
%U https://aclanthology.org/W19-3605/
%P 11-13

Download as File

Markdown (Informal)

[AspeRa: Aspect-Based Rating Prediction Based on User Reviews](https://aclanthology.org/W19-3605/) (Tutubalina et al., WiNLP 2019)

AspeRa: Aspect-Based Rating Prediction Based on User Reviews (Tutubalina et al., WiNLP 2019)

ACL

Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, and Ilya Shenbin. 2019. AspeRa: Aspect-Based Rating Prediction Based on User Reviews. In Proceedings of the 2019 Workshop on Widening NLP, pages 11–13, Florence, Italy. Association for Computational Linguistics.