Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Variations in relevance judgments and the measurement of retrieval effectiveness
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Liberal relevance criteria of TREC -: counting on negligible documents?
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Information retrieval in context: IRiX
ACM SIGIR Forum
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Slicing and dicing the information space using local contexts
IIiX Proceedings of the 1st international conference on Information interaction in context
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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Finding significant contextual features is a challenging task in the development of interactive information retrieval (IR) systems. This paper investigated a simple method to facilitate such a task by looking at aggregated relevance judgements of retrieved documents. Our study suggested that the agreement on relevance judgements can indicate the effectiveness of retrieved documents as the source of significant features. The effect of highly agreed documents gives us practical implication for the design of adaptive search models in interactive IR systems.