Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Traditional retrieval evaluation uses explicit relevance judgments which are expensive to collect. Relevance assessments inferred from implicit feedback such as click-through data...
Katja Hofmann, Bouke Huurnink, Marc Bron, Maarten ...
The performance of web search engines may often deteriorate due to the diversity and noisy information contained within web pages. User click-through data can be used to introduce...
In the past few years there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from ...
Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algor...
Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun ...
Previous efforts on event detection from the web have focused primarily on web content and structure data ignoring the rich collection of web log data. In this paper, we propose t...
Qiankun Zhao, Tie-Yan Liu, Sourav S. Bhowmick, Wei...
This paper addresses Named Entity Mining (NEM), in which we mine knowledge about named entities such as movies, games, and books from a huge amount of data. NEM is potentially use...
Although most of existing research usually detects events by analyzing the content or structural information of Web documents, a recent direction is to study the usage data. In th...