dc.contributor.author | Štrimaitis, Rokas | |
dc.contributor.author | Stefanovič, Pavel | |
dc.contributor.author | Ramanauskaitė, Simona | |
dc.contributor.author | Slotkienė, Asta | |
dc.date.accessioned | 2023-09-18T20:51:50Z | |
dc.date.available | 2023-09-18T20:51:50Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/153223 | |
dc.description.abstract | Business management requires constant decision-making. Usually, the selection of suitable partners for collaboration is very intuitive, experience-based skill or requires the usage of data analytics. The most important aspect to get an explainable recommendation for possible collaboration between companies is data. In this research, a company recommendation model is created to incorporate both the accounting system and publicly available data. The accounting system data is used to estimate the collaboration effectiveness of existing collaboration cases. The publicly available data include company registration data and news portal article texts. The news article data include the detection of the company mentioning, estimation of its sentiment and context category. Separate models were developed for different data extraction and analysis. A combined company recommendation system was designed. Model validation with gathered test cases demonstrated 70% recommendation accuracy. | eng |
dc.format | PDF | |
dc.format.extent | p. 93 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.ispartofseries | Vilnius University Proceedings vol. 31 2669-0233 | |
dc.relation.isreferencedby | Dimensions | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://www.mii.lt/damss/files/damss_2022.pdf | |
dc.source.uri | https://www.zurnalai.vu.lt/proceedings/issue/archive | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:148377608/datastreams/MAIN/content | |
dc.title | Company recommendation model: Empowering the accounting system and publicly available data | |
dc.type | Konferencijos pranešimo santrauka tarptautinėse DB / Conference presentation abstract in an international DB | |
dcterms.accessRights | This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 0 | |
dc.type.pubtype | T1 - Konferencijos pranešimo tezės tarptautinėse DB / Conference presentation abstract in an international DB | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
dc.subject.en | multi-label text data | |
dc.subject.en | similarity distance | |
dc.subject.en | classification | |
dc.subject.en | Lithuanian language | |
dc.subject.en | financial text data | |
dcterms.sourcetitle | DAMSS 2022: 13th conference on data analysis methods for software systems, Druskininkai, Lithuania, December 1–3, 2022 | |
dc.publisher.name | Vilniaus universitetas | |
dc.publisher.city | Vilnius | |
dc.identifier.doi | 10.15388/DAMSS.13.2022 | |
dc.identifier.elaba | 148377608 | |