RankBoost is a recently proposed algorithm for learning ranking functions. It is simple to implement and has strong justifications from computational learning theory. We describe...
Raj D. Iyer, David D. Lewis, Robert E. Schapire, Y...
We investigate the problem of ranking answers to a database query when many tuples are returned. We adapt and apply principles of probabilistic models from Information Retrieval f...
The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in mac...
Peer-to-peer file-sharing systems have hundreds of thousands of users sharing petabytes of data, however, their search functionality is limited. In general, query results contain...
Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging...
Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k com...
— Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, whic...
Keke Chen, Ya Zhang, Zhaohui Zheng, Hongyuan Zha, ...
Ranking function synthesis is a key aspect to the success of modern termination provers for imperative programs. While it is wellknown how to generate linear ranking functions for ...
Queries describe the users' search intent and therefore they play an essential role in the context of ranking for information retrieval and Web search. However, most of exist...
An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the seman...