Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
Information Processing and Management: an International Journal
I3R: a new approach to the design of document retrieval systems
Journal of the American Society for Information Science
Experiments with query acquisition and use in document retrieval systems
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
From user access patterns to dynamic hypertext linking
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
Automatically organizing bookmarks per contents
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
Integrating structured data and text: a relational approach
Journal of the American Society for Information Science
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
PowerBookmarks: a system for personalizable Web information organization, sharing, and management
WWW '99 Proceedings of the eighth international conference on World Wide Web
Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
When experts agree: using non-affiliated experts to rank popular topics
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Machine Learning
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
A machine learning approach to web personalization
A machine learning approach to web personalization
Efficient and Anonymous Web-Usage Mining for Web Personalization
INFORMS Journal on Computing
Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
A clickstream-based collaborative filtering personalization model: towards a better performance
Proceedings of the 6th annual ACM international workshop on Web information and data management
Identifying Variable-Length Meaningful Phrases with Correlation Functions
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
Adapting information retrieval to query contexts
Information Processing and Management: an International Journal
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
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Personalized web search incorporates an individual user's interests when deciding relevant results to return. While, most web search engines are usually designed to serve all users, without considering the interests of individual users. We propose a method to (re)rank the results from a search engine using a learned user profile, called a user interest hierarchy (UIH), from web pages that are of interest to the user. The user's interest in web pages will be determined implicitly, without directly asking the user. Experimental results indicate that our personalized ranking methods, when used with a popular search engine, can yield more potentially interesting web pages for individual users.