Spatial hypertext: designing for change
Communications of the ACM
XLibris: the active reading machine
CHI 98 Cconference Summary on Human Factors in Computing Systems
Proceedings of the 6th international conference on Intelligent user interfaces
The visual knowledge builder: a second generation spatial hypertext
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
The Journal of Machine Learning Research
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Proceedings of the 11th international conference on Intelligent user interfaces
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Studying the history of ideas using topic models
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Supporting document triage via annotation-based multi-application visualizations
Proceedings of the 10th annual joint conference on Digital libraries
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Interactive web search involves selecting which documents to read further and locating the parts of the documents that are relevant to the user's current activity. In this paper, we introduce UIMaP: User Interest Modeling and Personalization, a search task based personal user interest model to support users' information gathering tasks. The novelty of our approach lies in the use of topic modeling to generate fine-grained models of user interest and visualizations that direct user's attention to documents or parts of documents that match user's inferred interests. User annotations are used to help generate personalized visualizations for user's search tasks. Based on 1267 user annotations from 17 users, we show the performance comparisons of four different topic models: LDA+H, LDA+KL, LDA+JSD, and LDA+TopN.