Fast discovery of association rules
Advances in knowledge discovery and data mining
Variations in relevance judgments and the measurement of retrieval effectiveness
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Evaluating strategies for similarity search on the web
Proceedings of the 11th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithmic Clustering of Music
WEDELMUSIC '04 Proceedings of the Web Delivering of Music, Fourth International Conference
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ACM SIGIR Forum
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
On compressing frequent patterns
Data & Knowledge Engineering
On data mining, compression, and Kolmogorov complexity
Data Mining and Knowledge Discovery
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Using the wisdom of the crowds for keyword generation
Proceedings of the 17th international conference on World Wide Web
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
IEEE Transactions on Information Theory
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The results returned by a search, datamining or database engine often contains an overload of potentially interesting information. A daunting and challenging problem for a user is to pick out the useful information. In this paper we propose an interactive framework to efficiently explore and (re)rank the objects retrieved by such an engine, according to feedback provided on part of the initially retrieved objects. In particular, given a set of objects, a similarity measure applicable to the objects and an initial set of objects that are of interest to the user, our algorithm computes the k most similar objects. This problem, previously coined as 'cluster on demand' [10], is solved by transforming the data into a weighted graph. On this weighted graph we compute a relevance score between the initial set of nodes and the remaining nodes based upon random walks with restart in graphs.We apply our algorithm "Tell Me More" (TMM) on text, numerical and zero/one data. The results show that TMM for almost every experiment significantly outperforms a k-nearest neighbor approach.