Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The cluster hypothesis revisited
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A rank-aggregation approach to searching for optimal query-specific clusters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking search results using language models of query-specific clusters
Information Retrieval
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Evaluating text representations for retrieval of the best group of documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Geometric representations for multiple documents
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Quality-biased ranking of web documents
Proceedings of the fourth ACM international conference on Web search and data mining
Result diversification based on query-specific cluster ranking
Journal of the American Society for Information Science and Technology
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Journal of Artificial Intelligence Research
The optimum clustering framework: implementing the cluster hypothesis
Information Retrieval
Query-performance prediction and cluster ranking: two sides of the same coin
Proceedings of the 21st ACM international conference on Information and knowledge management
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An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types of cluster-relevance evidence; e.g., the query-similarity values of the cluster's documents and query-independent measures of the cluster. We use our method to re-rank an initially retrieved document list by ranking clusters that are created from the documents most highly ranked in the list. The resultant retrieval effectiveness is substantially better than that of the initial list for several lists that are produced by effective retrieval methods. Furthermore, our cluster ranking approach significantly outperforms state-of- the-art cluster ranking methods. We also show that our method can be used to improve the performance of (state-of- the-art) results-diversification methods.