Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
The concept of relevance in IR
Journal of the American Society for Information Science and Technology
Personalization in distributed e-learning environments
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Lucene in Action (In Action series)
Lucene in Action (In Action series)
Comparing and aggregating rankings with ties
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Automating metadata generation: the simple indexing interface
WWW '05 Proceedings of the 14th international conference on World Wide Web
AICT-SAPIR-ELETE '05 Proceedings of the Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/E-Learning on Telecommunications Workshop
Y!Q: contextual search at the point of inspiration
Proceedings of the 14th ACM international conference on Information and knowledge management
Use of contextualized attention metadata for ranking and recommending learning objects
CAMA '06 Proceedings of the 1st international workshop on Contextualized attention metadata: collecting, managing and exploiting of rich usage information
Repurposing learning object components
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
Finding appropriate learning objects: an empirical evaluation
ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries
LESSON: A system for lecture notes searching and sharing over Internet
Journal of Systems and Software
Metrics-based evaluation of learning object reusability
Software Quality Control
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Technologies that solve the scarce availability of learning objects have created the opposite problem: abundance of choice. The solution to that problem is relevance ranking. Unfortunately current techniques used to rank learning objects are not able to present the user with a meaningful ordering of the result list. This work interpret the Information Retrieval concept of Relevance in the context of learning object search and use that interpretation to propose a set of metrics to estimate the Topical, Personal and Situational relevance. These metrics are calculated mainly from usage and contextual information. An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric.