Topic-based recommendations for enterprise 2.0 resource sharing platforms

Authors:
Rafael Schirru;Stephan Baumann;Martin Memmel;Andreas Dengel
Affiliations:
German Research Center for Artificial Intelligence, Kaiserslautern, Germany and University of Kaiserslautern, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany and University of Kaiserslautern, Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany and University of Kaiserslautern, Kaiserslautern, Germany
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Year:
2011

Citing 9
Cited 0

Item-based collaborative filtering recommendation algorithms

Proceedings of the 10th international conference on World Wide Web
Document clustering based on non-negative matrix factorization

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
Improving recommendation lists through topic diversification

WWW '05 Proceedings of the 14th international conference on World Wide Web
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Workshop on social web and knowledge management (SWKM2008)

Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval

Introduction to Information Retrieval
Topic-based personalized recommendation for collaborative tagging system

Proceedings of the 21st ACM conference on Hypertext and hypermedia
Domain-specific identification of topics and trends in the blogosphere

ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects

Quantified Score

Hi-index	0.00

Visualization

Abstract

Companies increasingly often deploy social media technologies to foster the knowledge transfer between employees. As the amount of resources in such systems is usually large there is a need for recommender systems that provide personalized information access. Traditional recommender systems suffer from sparsity issues in such environments and do not take the users' different topics of interest into account. We propose a topic-based recommender system tackling these issues. Our approach applies algorithms from the domain of topic detection and tracking on the metadata profiles of the users' preferred resources to identify their interest topics. Every topic is represented as a weighted term vector that can be used to retrieve unknown, relevant resources matching the users' topics of interest. An evaluation of the approach has shown that our method retrieves on-topic resources with a high precision.