Seeing the whole in parts: text summarization for web browsing on handheld devices
Proceedings of the 10th international conference on World Wide Web
A brief survey of web data extraction tools
ACM SIGMOD Record
Comparing Hierarchical Data in External Memory
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Analysis of anchor text for web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Web Behaviormetric User Profiling Concept
EC-Web '08 Proceedings of the 9th international conference on E-Commerce and Web Technologies
User Profiling for Web Search Based on Biological Fluctuation
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
No Code Required: Giving Users Tools to Transform the Web
No Code Required: Giving Users Tools to Transform the Web
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Browsing activities are an important source of information to build profiles of the user interests and personalize the human-computer interaction during information seeking tasks. Visited pages are easily collectible, e.g., from browsers' histories and toolbars, or desktop search tools, and they often contain documents related to the current user needs. Nevertheless, menus, advertisements or pages that cover multiple topics affect negatively the advantages of an implicit feedback technique that exploits these data to build and keep updated user profiles. This work describes a technique to collect text relevant to the current needs from sequences of pages visited by the user. The evaluation shows how it outperforms other techniques that consider the whole page contents. We also introduce an improvement based on machine learning techniques that is currently under evaluation.