HUNG CHAU's blog

Rose Catherine from Language Technologies Institute, Carnegie Mellon University presented the talk Personalized Recommendations using Knowledge Graphs by 09/23/2016. At the beginning, Rose introduced general ideas about personalized recommendations and gave some examples such as Amazon recommending customers products they would like to buy and introducing movies to users based on their favorite ones. And then, Rose focused on improving the performance of recommender systems using knowledge graphs (KGs). She introduced other works that use the combination of content-based and collaborative filtering techniques and connected concept-based KGs on recommendations. To improve the current methods, Rose's research group investigates three techniques for making recommendations. Those KG-based recommendations use a probabilistic logic system called ProPPR which stands for Programming with Personalized PageRank. The first approach is EntitySim tha

In this seminar, prof essor Grace Hui Yang presents a dynamic information retrieval model which helps users explore the information space in order to find out which documents are relevant and which aren't, satisfying their information need. The dynamic IR task aims to find relevant documents for a session of multiple queries. It happens when information needs are complex, vague, evolving, often containing multiple subtopics. A dynamic system is one which changes or adapts over time, based on a sequence of events. While static IR does not learn directly from users and the parameters are updated periodically, dynamic IR is developed from interactive IR which exploits users' feedback to give recommendation or improve the search result or optimize ranking, which is called dynamic ranking principle. But interactive IR just treat interaction independently and response to immediate feedback. Dynamic IR tries to optimize over all interaction, uses it for long term gain and models

In this seminar, Igor presents an idea about organizing heterogeneous educational resources on the web into structure alike to a textbook or course. Thanks to the structure, engines might be able to allow learners to navigate a sequence of webpages that take them from their prior knowledge to material they want to learn. He gives an opinion that educational resources on the internet are diversity; they could be articles, lecture notes, tutorials, slides, etc. And those materials are provided by various kind of creators from different perspectives, and thus feed a variety of learners who do not necessarily rely on textbooks. To approach this task, Igor first presents a document as a bag-of-technical-terms consisting of two multi sets, a set of Explained terms and a set of Assumed terms. Explained : the term appears in the context and is explained to be understood by readers.  Assumed : the term corresponding to a explained term is assumed to be familiar with readers, and is re

I am a first-year Ph.D. student at School of Information Sciences, University of Pittsburgh. I received my Master and Bachelor degrees from University of Information Technology. I am very interested in building Adaptive Educational Systems, Intelligent Tutoring Systems, Educational Data Mining, Semantic Information Retrieval, User Modeling and Cognitive Systems. I am currently working at  the Personalized Adaptive Web Systems Lab (PAWS) . My advisor is  Dr. Peter Brusilovsky . Currently, I have been taking part in the projects Open Corpus Personalized Learning and cWadeIn. Here is my homepage HUNG K CHAU