Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost....
Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang...
Collaborative Filtering, considered by many researchers as the most important technique for information filtering, has been extensively studied by both academic and industrial co...
With more and more large networks becoming available, mining and querying such networks are increasingly important tasks which are not being supported by database models and query...
Subgraph patterns are widely used in graph classification, but their effectiveness is often hampered by large number of patterns or lack of discrimination power among individual p...
Keyphrases are short phrases that reflect the main topic of a document. Because manually annotating documents with keyphrases is a time-consuming process, several automatic appro...
Katja Hofmann, Manos Tsagkias, Edgar Meij, Maarten...
We investigate the problem of determining the polarity of sentiments when one or more occurrences of a negation term such as “not” appear in a sentence. The concept of the sco...
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the au...
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
We propose a General Markov Framework for computing page importance. Under the framework, a Markov Skeleton Process is used to model the random walk conducted by the web surfer on...
Bin Gao, Tie-Yan Liu, Zhiming Ma, Taifeng Wang, Ha...