C4.5: programs for machine learning
C4.5: programs for machine learning
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Web montage: a dynamic personalized start page
Proceedings of the 11th international conference on World Wide Web
Information Retrieval
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast multi-level adaptation for interactive autonomous characters
ACM Transactions on Graphics (TOG)
Automatic new topic identification using multiple linear regression
Information Processing and Management: an International Journal
Construction of Ontology-Based User Model for Web Personalization
UM '07 Proceedings of the 11th international conference on User Modeling
Goal-directed site-independent recommendations from passive observations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Experiments on the preference-based organization interface in recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Clustering web users based on browsing behavior
AMT'10 Proceedings of the 6th international conference on Active media technology
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
Conqueries: an agent that supports query expansion
ICWE'05 Proceedings of the 5th international conference on Web Engineering
Discovering stages in web navigation
UM'05 Proceedings of the 10th international conference on User Modeling
Predicting web information content
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
The effects of negative interaction feedback in a web navigation assistant
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: users and contexts of use - Volume Part III
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There are many recommender systems that are designed to help users find relevant information on the web. To produce recommendations that are relevant to an individual user, many of these systems first attempt to learn a model of the user's browsing behavior. This paper presents a novel method for learning such a model from a set of annotated web logs--i.e., web logs that are augmented with the user's assessment of whether each webpage is an information content (IC) page (i.e., contains the information required to complete her task). Our systems use this to learn what properties of a webpage, within a sequence, identify such IC-pages, and similarly what "browsing properties" characterize the words on such pages ("IC-words"). As these methods deal with properties of web pages (or of words), rather than specific URLs (words), they can be used anywhere throughout the web; i.e., they are not specific to a particular website, or a particular task. This paper also describes the enhanced browser, aie, that we designed and implemented for collecting these annotated web logs, and an empirical study we conducted to investigate the effectiveness of our approach. This empirical evidence shows that our approach, and our algorithms, work effectively.