Existing document filtering systems learn user profiles based on user relevance feedback on documents. In some cases, users may have prior knowledge about what features are important. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature“Language: Spanish”; a researcher focusing on HIV knows an article with the medical subj...

In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing pairwise loss. We show minimization problem involves dependent random variables and provide theoretical analysis proving consistency empirical risk in worst case where all users choose minimal number positive negative items. further derive Ne...

To investigate whether more concise Natural Language feedback improves learning, we developed two Natural Language generators (DIAG-NLP1 and DIAG-NLP2), to provide feedback in an Intelligent Tutoring System that teaches troubleshooting. We systematically evaluated them in a three way comparison that included the original system, which generates overly repetitive feedback. We found that DIAG-NLP...

We have proposed a computer-assisted language learning (CALL) system, called Postech English Speaking Assessment and Assistant (PESAA) for non-native English learners, especially Koreans, to improve their overall language skills. PESAA is an automatic feedback system for speaking English that integrates both pronunciation and prosody assessments. The system has three error-feedback modules: the...

Experiments on the effectiveness of relevance feedback with real users are time-consuming and expensive. This makes simulation for rapid testing desirable. We define a user model, which helps to quantify some interaction decisions involved in simulated relevance feedback. First, the relevance criterion defines the relevance threshold of the user to accept documents as relevant to his/her needs....