Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Proceedings of the 13th international conference on World Wide Web
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A personalized search engine based on Web-snippet hierarchical clustering
Software—Practice & Experience
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Using trees to depict a forest
Proceedings of the VLDB Endowment
Si-Fi: interactive similar item finder
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Cluster generation and cluster labelling for web snippets: a fast and accurate hierarchical solution
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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Search result clustering provides an intuitive overview toward information contained in the search result. The goal of this research is to implement a clustering engine to provide search result clustering for various search tasks retrieving items, or objects whose contents do not contain descriptive text. Content-based similarity measures used for traditional clustering engines are not suitable for general measure, because of its domain-specific nature and lack of descriptiveness. To remedy the problems, we exploit user feedback information to measure similarity between items. As the first approach to use user feedback information to measure similarity between general items to cluster them, we explore similarity models and algorithms suitable for clustering. To realize usefulness of the presented clustering method, performance of the clustering is evaluated using some real-world data sets. The presented method produces more accurate clusters than clustering methods based on traditional content-based measures do. After optimizing the method, a web-based application based on the clustering method is implemented. The flexibility of the implemented system and the application enables search results clustering to be applied to search results containing various type of objects.