Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Capturing knowledge of user preferences: ontologies in recommender systems
Proceedings of the 1st international conference on Knowledge capture
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Detect and track latent factors with online nonnegative matrix factorization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using latent topics to enhance search and recommendation in Enterprise Social Software
Expert Systems with Applications: An International Journal
| Hi-index | 0.01 |
Nowadays, many companies deploy social media technologies to foster the knowledge transfer in the enterprise. As the amount of available content in such systems grows, there is an increasing need for recommender systems that provide recommendations according to the knowledge workers' needs and preferences. We propose a topic-based recommender system for Enterprise 2.0 resource sharing platforms. The system identifies the knowledge workers' short-term and long-term topics of interest by applying algorithms from the domain of topic detection and tracking and generates recommendations with a high degree of inter-topic diversity.