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
Novelty and topicality in interactive information retrieval
Journal of the American Society for Information Science and Technology
Proceedings of the Second ACM International Conference on Web Search and Data Mining
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
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In this paper, we propose efficient algorithms for result diversification over indexed multidimensional data. We develop algorithms under the prism of a centralized approach, as in a database. Specifically, we rely on widely used multidimensional indexes, like the R-tree. In principle, our schemes adopt a maximal marginal relevance (MMR) ranking strategy and leverage interchange and greedy diversification techniques. Hitherto, mostly combinatorial aspects of this problem have been considered which require scanning the entire data, and therefore, existing solutions are costly.