ACM Computing Surveys (CSUR)
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
An Approach for Semantic Search by Matching RDF Graphs
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
The singular value decomposition in multivariate statistics
ACM SIGNUM Newsletter
The S-Space package: an open source package for word space models
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Extracting authoring information based on keywords and semantic search
Proceedings of the 1st International Conference on Intelligent Semantic Web-Services and Applications
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With vast amounts of data being produced, present world is overwhelmed with information and searching for appropriate content has turned out to be harder than ever before. Semantics, which typically focuses on the relationship between signifiers, such as words, phrases, signs and symbols, and what they stand for is now being used more and more in search engines to provide the user with more meaningful content. Further it is no more the case that users are interested in search results that the majority of users would agree to, but are more interested in results being personalized to them. In this research paper we present iSeS: Intelligent Semantic Search Framework, which is a search framework, a custom web site or an application can adapt. We focus on using underlying semantics of the content being indexed in providing more meaningful search results personalized to each user. We look into both latent semantic indexing and metadata extraction based methods for providing semantically rich search results. Collaborative filtering and how it is used to personalize search results is also explored in this paper.