Making large-scale support vector machine learning practical
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Information Systems Frontiers
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
An algorithm for inconsistency resolving in recommendation web-based systems
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Proceedings of the 8th International Conference on Semantic Systems
Semantic metadata in the news production process: achievements and challenges
Proceeding of the 16th International Academic MindTrek Conference
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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Fast absorption of information is a necessity for modern information workers. In the short-lived news area, information is a perishable good. While online news websites can speed up the publication of current events compared to traditional newspapers, reading can be more exhausting as online readers have to navigate through websites by clicking on abstracts or headlines before viewing the underlying article. Online shops use personalization methods in order to improve product selection. So far, most types of personalization are offered by website owners and are therefore bound to a specific website. This work presents NewsRec, a client side personal recommendation system for news websites, that supports information workers during their usage of online news websites. Design aspects are discussed and empirical results are shown.