Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising. We formulate the ranking of relevant terms as a...
An approach to target-based image retrieval is described based on on-line rank-based learning. User feedback obtained via interaction with 2D image layouts provides qualitative co...
Background: Finding relevant articles from PubMed is challenging because it is hard to express the user’s specific intention in the given query interface, and a keyword query ty...
Hwanjo Yu, Taehoon Kim, Jinoh Oh, Ilhwan Ko, Sungc...