<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="berbatova-2019-overview">
    <titleInfo>
        <title>Overview on NLP Techniques for Content-based Recommender Systems for Books</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Melania</namePart>
        <namePart type="family">Berbatova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Student Research Workshop Associated with RANLP 2019</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Branislava</namePart>
            <namePart type="family">Šandrih</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Recommender systems are an essential part of today’s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads’ website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.</abstract>
    <identifier type="citekey">berbatova-2019-overview</identifier>
    <identifier type="doi">10.26615/issn.2603-2821.2019_009</identifier>
    <location>
        <url>https://aclanthology.org/R19-2009</url>
    </location>
    <part>
        <date>2019-09</date>
        <extent unit="page">
            <start>55</start>
            <end>61</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Overview on NLP Techniques for Content-based Recommender Systems for Books
%A Berbatova, Melania
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Šandrih, Branislava
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2019
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F berbatova-2019-overview
%X Recommender systems are an essential part of today’s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads’ website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.
%R 10.26615/issn.2603-2821.2019_009
%U https://aclanthology.org/R19-2009
%U https://doi.org/10.26615/issn.2603-2821.2019_009
%P 55-61

Markdown (Informal)

[Overview on NLP Techniques for Content-based Recommender Systems for Books](https://aclanthology.org/R19-2009) (Berbatova, RANLP 2019)

• Overview on NLP Techniques for Content-based Recommender Systems for Books (Berbatova, RANLP 2019)

ACL

• Melania Berbatova. 2019. Overview on NLP Techniques for Content-based Recommender Systems for Books. In Proceedings of the Student Research Workshop Associated with RANLP 2019, pages 55–61, Varna, Bulgaria. INCOMA Ltd..