Hybrid parallel approach for personalized literature recommendation system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092821" target="_blank" >RIV/61989100:27240/14:86092821 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092821

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CASoN.2014.6920428" target="_blank" >http://dx.doi.org/10.1109/CASoN.2014.6920428</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CASoN.2014.6920428" target="_blank" >10.1109/CASoN.2014.6920428</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid parallel approach for personalized literature recommendation system

Popis výsledku v původním jazyce

Researchers regularly access and review large amounts of literatures. In the previous work, we presented a bookmarklet-triggered literature sharing system, which combines bibliography functionalities along with DOI content negotiation services. In this paper, we have made secondary development work to integrate literature recommendation functionalities into this system. We introduce a hybrid approach in parallel to recommend related articles to researchers. First, we collect a large amount of publishedand new articles using crawlers and RSS listeners to address cold start issue. Second, we adopt Latent Dirichlet Allocation (LDA) as the topic model to category literatures. For one kind of literatures related to researchers' interest, we use collaborative filtering techniques to make further analysis based on implicit user feedbacks in this system. Finally, we take matrix factorization with Alternating Least Squares (ALS) in parallel to compute the top-N recommendations per user. 2014 I

Název v anglickém jazyce

Hybrid parallel approach for personalized literature recommendation system

Popis výsledku anglicky

Researchers regularly access and review large amounts of literatures. In the previous work, we presented a bookmarklet-triggered literature sharing system, which combines bibliography functionalities along with DOI content negotiation services. In this paper, we have made secondary development work to integrate literature recommendation functionalities into this system. We introduce a hybrid approach in parallel to recommend related articles to researchers. First, we collect a large amount of publishedand new articles using crawlers and RSS listeners to address cold start issue. Second, we adopt Latent Dirichlet Allocation (LDA) as the topic model to category literatures. For one kind of literatures related to researchers' interest, we use collaborative filtering techniques to make further analysis based on implicit user feedbacks in this system. Finally, we take matrix factorization with Alternating Least Squares (ALS) in parallel to compute the top-N recommendations per user. 2014 I

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2014 6th International Conference on Computational Aspects of Social Networks, CASoN 2014

ISBN

978-1-4799-5940-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

31-36

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Porto

Datum konání akce

30. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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