Information access in complex, poorly structured information spaces
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
Learning users' interests by unobtrusively observing their normal behavior
Proceedings of the 5th international conference on Intelligent user interfaces
SUITOR: an attentive information system
Proceedings of the 5th international conference on Intelligent user interfaces
Proceedings of the 6th international conference on Intelligent user interfaces
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper, the stable personal browsing patterns shown in Internet surfing are utilized to determine the users' preference on specific content. To be more specific, they are used to calculate the so called implicit ratings. We performed an experiment on all possible combinations of the implicit indicators to pick out the most significant indicators— elements of user browsing patterns. A thorough analysis and comparison are carried out before four indicators are selected as the input of an Artificial Neural Network which is adopted to calculate the implicit ratings. The mechanism of the implicit rating calculation is integrated into an educational resource sharing system as a featured module and works well.