Using latent topics to enhance search and recommendation in Enterprise Social Software

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dc.contributor.author Christidis, K en
dc.contributor.author Mentzas, G en
dc.contributor.author Apostolou, D en
dc.date.accessioned 2014-03-01T02:14:53Z
dc.date.available 2014-03-01T02:14:53Z
dc.date.issued 2012 en
dc.identifier.issn 09574174 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30165
dc.subject Enterprise Social Software en
dc.subject Latent Dirichlet Allocation en
dc.subject Latent topic models en
dc.subject Recommender systems en
dc.subject Search en
dc.subject.other Folksonomies en
dc.subject.other Informal interactions en
dc.subject.other Information resource en
dc.subject.other Knowledge structures en
dc.subject.other Latent Dirichlet allocation en
dc.subject.other Latent topic models en
dc.subject.other Open sources en
dc.subject.other Organizational system en
dc.subject.other Probabilistic topic models en
dc.subject.other Query results en
dc.subject.other Search en
dc.subject.other Small and medium enterprise en
dc.subject.other Social software en
dc.subject.other Tag recommendations en
dc.subject.other User activity en
dc.subject.other Web 2.0 en
dc.subject.other Industry en
dc.subject.other Recommender systems en
dc.subject.other Semantic Web en
dc.subject.other Statistics en
dc.subject.other Open systems en
dc.title Using latent topics to enhance search and recommendation in Enterprise Social Software en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.eswa.2012.02.073 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.eswa.2012.02.073 en
heal.publicationDate 2012 en
heal.abstract Enterprise Social Software refers to open and flexible organizational systems and tools which utilize Web 2.0 technologies to stimulate participation through informal interactions. A challenge in Enterprise Social Software is to discover and maintain over time the knowledge structure of topics found relevant to the organization. Knowledge structures, ranging in formality from ontologies to folksonomies, support user activity by enabling users to categorize and retrieve information resources. In this paper we enhance the search and recommendation functionalities of Enterprise Social Software by extending their knowledge structures with the addition of underlying hidden topics which we discover using probabilistic topic models. We employ Latent Dirichlet Allocation in order to elicit hidden topics and use the latter to assess similarities in resource and tag recommendation as well as for the expansion of query results. As an application of our approach we have extended the search and recommendation facilities of an open source Enterprise Social Software system which we have deployed and evaluated in five knowledge-intensive small and medium enterprises. © 2012 Elsevier Ltd. All rights reserved. en
heal.journalName Expert Systems with Applications en
dc.identifier.doi 10.1016/j.eswa.2012.02.073 en
dc.identifier.volume 39 en
dc.identifier.issue 10 en
dc.identifier.spage 9297 en
dc.identifier.epage 9307 en

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