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
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
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Predicting diverse subsets using structural SVMs
Proceedings of the 25th international conference on Machine learning
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the fourth ACM international conference on Web search and data mining
Structured learning of two-level dynamic rankings
Proceedings of the 20th ACM international conference on Information and knowledge management
Structured learning of two-level dynamic rankings
Proceedings of the 20th ACM international conference on Information and knowledge management
Online learning to diversify from implicit feedback
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-margin learning of submodular summarization models
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Temporal corpus summarization using submodular word coverage
Proceedings of the 21st ACM international conference on Information and knowledge management
Toward whole-session relevance: exploring intrinsic diversity in web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Diversified relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Interactive exploratory search for multi page search results
Proceedings of the 22nd international conference on World Wide Web
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For ambiguous queries, conventional retrieval systems are bound by two conflicting goals. On the one hand, they should diversify and strive to present results for as many query intents as possible. On the other hand, they should provide depth for each intent by displaying more than a single result. Since both diversity and depth cannot be achieved simultaneously in the conventional static retrieval model, we propose a new dynamic ranking approach. In particular, our proposed two-level dynamic ranking model allows users to adapt the ranking through interaction, thus overcoming the constraints of presenting a one-size-fits-all static ranking. In this model, a user's interactions with the first-level ranking are used to infer this user's intent, so that second-level rankings can be inserted to provide more results relevant to this intent. Unlike previous dynamic ranking models, we provide an algorithm to efficiently compute dynamic rankings with provable approximation guarantees. We also propose the first principled algorithm for learning dynamic ranking functions from training data. In addition to the theoretical results, we provide empirical evidence demonstrating the gains in retrieval quality over conventional approaches.