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

  • Authors:
  • Konstantinos Christidis;Gregoris Mentzas;Dimitris Apostolou

  • Affiliations:
  • National Technical University of Athens, Zografou, 157 80 Athens, Greece;National Technical University of Athens, Zografou, 157 80 Athens, Greece;Department of Informatics, University of Piraeus, Piraeus 185 34, Greece

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

Quantified Score

Hi-index 12.06

Visualization

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.