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

Christidis, K; Mentzas, G; Apostolou, D

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