A Unified Approach to Researcher Profiling
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic Detection by Clustering Keywords
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Developing a Recommendation Web Service for a Federation of Learning Repositories
INCOS '09 Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems
Measuring Semantic Similarity between Words Using Wikipedia
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
Collaborative filtering recommender systems
The adaptive web
Dataset-driven research for improving recommender systems for learning
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
PSLC datashop: a data analysis service for the learning science community
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
| Hi-index | 0.00 |
In an offline evaluation of recommender systems, data sets have been extensively used to measure the performance of recommender systems through statistical analysis. However, many data sets are domain and application dependent and cannot be engaged in different domains. This paper presents the construction of data sets for the offline evaluation of a scholar's recommender system that suggests papers to scholars based on their background knowledge. We design a cross-validation approach to reduce the risk of false interpretations by relying on multiple independent sources of information. Our approach addresses four important issues including the privacy and diversity of knowledge resources, the quality of knowledge, and the timely knowledge. The resulting data sets represent the instance of scholar's background knowledge in clusters of learning themes, which can be used to measure the performance of the scholar's recommender system.