Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
A multilevel approach to intelligent information filtering: model, system, and evaluation
ACM Transactions on Information Systems (TOIS)
Learning personal preferences on online newspaper articles from user behaviors
Selected papers from the sixth international conference on World Wide Web
Dynamic HTML: the definitive reference
Dynamic HTML: the definitive reference
A personalized television listings service
Communications of the ACM
Capturing human intelligence in the net
Communications of the ACM
Personalization on the Net using Web mining: introduction
Communications of the ACM
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
SIFT: a tool for wide-area information dissemination
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Implicit interaction profiling for recommending spatial content
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Using task context to improve programmer productivity
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Browsing a website with topographic hints
AVI '08 Proceedings of the working conference on Advanced visual interfaces
Behavior-driven visualization recommendation
Proceedings of the 14th international conference on Intelligent user interfaces
Towards a model of implicit feedback for Web search
Journal of the American Society for Information Science and Technology
Analysis of internet users' interests based on windows GUI messages
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: applications and services
Inferring word relevance from eye-movements of readers
Proceedings of the 16th international conference on Intelligent user interfaces
Using browser interaction data to determine page reading behavior
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Presentation of dynamic maps by estimating user intentions from operation history
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Using data mining for modeling personalized maps
ER'05 Proceedings of the 24th international conference on Perspectives in Conceptual Modeling
User see, user point: gaze and cursor alignment in web search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Improving search result summaries by using searcher behavior data
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Web browsing behavior analysis and interactive hypervideo
ACM Transactions on the Web (TWEB)
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In the area of information retrieval and information filtering, relevance feedback is a popular technique which searches similar documents based on the documents browsed by the user. If the user wants to conduct relevance feedback on demand, which means the user wants to see similar documents while reading a document, the existing user profiling techniques cannot acquire keywords in high precision that the user is interested in at such a short time. This paper proposes a method for extracting text parts which the user might be interested in from the whole text of the Web page based on the user's mouse operation in the Web browser. The objective of this research is to (1) find what kind of mouse operation represent users' interests, (2) see the effectiveness of the found mouse operation in selecting keywords, and (3) compare our method with tf-idf, which is the most fundamental method used in many user profiling systems. From the user experiment, the precision to select keywords of our method is about 1.4 times compared with that of tf-idf.