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• W2039401954 abstract "Highlights? Application of latent topic models for search and recommendation in Enterprise Social Software. ? Generation and refinement of enterprise knowledge structures with latent topic models. ? Item to item collaborative and content based recommendations with Latent Dirichlet Allocation. 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." @default.
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• W2039401954 date "2012-08-01" @default.
• W2039401954 modified "2023-09-27" @default.
• W2039401954 title "Using latent topics to enhance search and recommendation in Enterprise Social Software" @default.
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• W2039401954 doi "https://doi.org/10.1016/j.eswa.2012.02.073" @default.
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