MMR (Maximum Marginal Relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches ...
The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based re...
With the continuing advances in data storage and communication technology, there has been an explosive growth of music information from different application domains. As an effe...
Bingjun Zhang, Jialie Shen, Qiaoliang Xiang, Ye Wa...
Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semiautomatic, e.g. an expert must tune some parameters ...
With the ubiquity of information networks and their broad applications, the issue of similarity computation between entities of an information network arises and draws extensive r...
Adapting to rank address the the problem of insufficient domainspecific labeled training data in learning to rank. However, the initial study shows that adaptation is not always...
Keke Chen, Jing Bai, Srihari Reddy, Belle L. Tseng
—Protein binding sites are often represented by means of graphs capturing their most important geometrical and physicochemical properties. Searching for structural similarities a...
Imen Boukhris, Zied Elouedi, Thomas Fober, Marco M...
Abstract. This paper shows how Wikipedia and the semantic knowledge it contains can be exploited for document clustering. We first create a concept-based document representation b...
Anna Huang, David N. Milne, Eibe Frank, Ian H. Wit...
We present an efficient pixel-sampling technique for histogram-based search. Given a template image as a query, a typical histogram-based algorithm aims to find the location of ...
1 We consider the problem of similarity search in applications where the cost of computing the similarity between two records is very expensive, and the similarity measure is not a...
Chris Jermaine, Fei Xu, Mingxi Wu, Ravi Jampani, T...