In a categorized information space, predicting users' information needs at the category level can facilitate personalization, caching and other topic-oriented services. This ...
We compare standard global IR searching with user-centric localized techniques to address the database selection problem. We conduct a series of experiments to compare the retriev...
We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The a...
We develop a method for predicting query performance by computing the relative entropy between a query language model and the corresponding collection language model. The resultin...
This paper follows a formal approach to information retrieval based on statistical language models. By introducing some simple reformulations of the basic language modeling approa...