Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Node similarity in the citation graph
Knowledge and Information Systems
Research Paper Recommender Systems: A Random-Walk Based Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Communications of the ACM
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
Research paper recommender systems: a subspace clustering approach
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Enhancing search applications by utilizing mind maps
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Proposing an ISO/IEC 15504-2 compliant method for process capability/maturity models customization
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
| Hi-index | 0.00 |
In a previous paper we presented various ideas on how information retrieval on mind maps could enhance applications such as expert systems, search engines and recommender systems. In this paper we present the first research results. In a brief experiment we researched link analysis respectively citation analysis, if applied to mind maps, is suitable to calculate document relatedness. The basic idea is that if two documents A and B are linked by the same mind map, these documents are likely to be related. This information could be used by item-based document recommender systems. In the example, document B could be recommended to those users interested in document A. In addition, we propose that those documents linked in high proximity within a mind map are more closely related than those documents linked in lower proximity. The results of our experiment support our ideas. It seems that link analysis applied to mind maps can be used for determining the relatedness of documents and therefore for improving document recommender systems.