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
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Probabilistic models of ranking novel documents for faceted topic retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Top-k retrieval using facility location analysis
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Mining subtopics from text fragments for a web query
Information Retrieval
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A set of words is often insufficient to express a user's information need. In order to account for various information needs associated with a query, diversification seems to be a reasonable strategy. By diversifying the result set, we increase the probability of results being relevant to the user's information needs when the given query is ambiguous. A diverse result set must contain a set of documents that cover various subtopics for a given query. We propose a graph based method which exploits the link structure of the web to return a ranked list that provides complete coverage for a query. Our method not only provides diversity to the results set, but also avoids excessive redundancy. Moreover, the probability of relevance of a document is conditioned on the documents that appear before it in the result list. We show the effectiveness of our method by comparing it with a query-likelihood model as the baseline.