Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Incremental relevance feedback
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A case for interaction: a study of interactive information retrieval behavior and effectiveness
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine Learning
Modern Information Retrieval
Multimedia Tools and Applications
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Nowadays, the World Wide Web offers public search services by a number of Internet search engine companies e.g. Google [16], Yahoo! [17], etc. They own their internal ranking algorithms, which may be designed for either general-purpose information and/or specific domains. In order to fight for bigger market share, they have developed advanced tools to facilitate the algorithms through the use of Relevance Feedback (RF) e.g. Google’s Toolbar. Experienced by the black-box tests of the RF toolbar, all in all, they can acquire simple and individual RF contribution. As to this point, in this paper, we have proposed a collaboratively shared Information Retrieval (IR) model to complement the conventional IR approach (i.e. objective) with the collaborative user contribution (i.e. subjective). Not only with RF and group relevance judgments, our proposed architecture and mechanisms provide a unified way to handle general purpose textual information (herein, we consider e-Learning related documents) and provide advanced access control features [15] to the overall system.