Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/23678
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCoelho, J.-
dc.contributor.authorNeto, A.-
dc.contributor.authorTavares, M.-
dc.contributor.authorCoutinho, C.-
dc.contributor.authorOliveira, J.-
dc.contributor.authorRibeiro, R.-
dc.contributor.authorBatista, F.-
dc.contributor.editorCucchiara, R., Fred, A., & Filipe, J.-
dc.date.accessioned2021-12-10T10:57:44Z-
dc.date.available2021-12-10T10:57:44Z-
dc.date.issued2021-
dc.identifier.isbn978-989-758-533-3-
dc.identifier.issn2184-3228-
dc.identifier.urihttp://hdl.handle.net/10071/23678-
dc.description.abstractSearch engines are being extensively used by Mobile App Stores, where millions of users world-wide use them every day. However, some stores still resort to simple lexical-based search engines, despite the recent advances in Machine Learning, Information Retrieval, and Natural Language Processing, which allow for richer semantic strategies. This work proposes an approach for semantic search of mobile applications that relies on transformer-based language models, fine-tuned with the existing textual information about known mobile applications. Our approach relies solely on the application name and on the unstructured textual information contained in its description. A dataset of about 500 thousand mobile apps was extended in the scope of this work with a test set, and all the available textual data was used to fine-tune our neural language models. We have evaluated our models using a public dataset that includes information about 43 thousand applications, and 56 manually annotated non- exact queries. The results show that our model surpasses the performance of all the other retrieval strategies reported in the literature. Tests with users have confirmed the performance of our semantic search approach, when compared with an existing deployed solution.eng
dc.language.isoeng-
dc.publisherSCITEPRESS – Science and Technology Publications, Lda-
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/339703/EU-
dc.relationUIDB/50021/2020-
dc.rightsopenAccess-
dc.subjectSemantic searcheng
dc.subjectWord embeddingseng
dc.subjectElasticSearcheng
dc.subjectMobile applicationseng
dc.subjectTransformer-based modelseng
dc.titleTransformer-based language models for semantic search and mobile applications retrievaleng
dc.typeconferenceObject-
dc.event.title13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR-
dc.event.typeConferênciapt
dc.event.date2021-
dc.pagination225 - 232-
dc.peerreviewedyes-
dc.journalProceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management-
dc.volume1-
degois.publication.firstPage225-
degois.publication.lastPage232-
degois.publication.titleTransformer-based language models for semantic search and mobile applications retrievaleng
dc.date.updated2021-12-10T10:53:49Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.5220/0010657300003064-
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-82724-
Appears in Collections:CTI-CRI - Comunicações a conferências internacionais
ISTAR-CRI - Comunicações a conferências internacionais
IT-CRI - Comunicações a conferências internacionais

Files in This Item:
File Description SizeFormat 
conferenceobject_82724.pdfVersão Aceite274,2 kBAdobe PDFView/Open


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis Logotipo do Orcid 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.