Show simple item record

dc.contributor.authorCordobés de la Calle, Héctor 
dc.contributor.authorChiroque, Luis F. 
dc.contributor.authorFernández Anta, Antonio 
dc.contributor.authorGarcía, Rafael
dc.contributor.authorMorere, Philippe 
dc.contributor.authorOrnella, Lorenzo
dc.contributor.authorPérez, Fernando
dc.contributor.authorSantos, Agustín 
dc.date.accessioned2021-07-13T09:25:24Z
dc.date.available2021-07-13T09:25:24Z
dc.date.issued2015-03
dc.identifier.issn1989-1660
dc.identifier.urihttp://hdl.handle.net/20.500.12761/8
dc.description.abstractRecommendation engines (RE) are becoming highly popular, e.g., in the area of e-commerce. A RE offers new items (products or content) to users based on their profile and historical data. The most popular algorithms used in RE are based on collaborative filtering. This technique makes recommendations based on the past behavior of other users and the similarity between users and items. In this paper we have evaluated the performance of several RE based on the properties of the networks formed by users and items. The RE use in a novel way graph theoretic concepts like edges weights or network flow. The evaluation has been conducted in a real environment (ecosystem) for recommending apps to smartphone users. The analysis of the results allows concluding that the effectiveness of a RE can be improved if the age of the data, and if a global view of the data is considered. It also shows that graph-based RE are effective, but more experiments are required for a more accurate characterization of their properties.
dc.language.isoeng
dc.publisherImaI-Software
dc.titleEmpirical Comparison of Graph-based Recommendation Engines for an Apps Ecosystemen
dc.typejournal article
dc.journal.titleInternational Journal of Interactive Multimedia and Artificial Intelligence
dc.type.hasVersionVoR
dc.rights.accessRightsopen access
dc.volume.number3
dc.issue.number2
dc.identifier.doi10.9781/ijimai.2015.324
dc.page.final39
dc.page.initial33
dc.subject.keywordRecommendation engines
dc.subject.keywordsmartphone apps
dc.subject.keywordgraph theory
dc.subject.keywordcollaborative filtering
dc.subject.keywordflow algorithms
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/1000


Files in this item

This item appears in the following Collection(s)

Show simple item record