Max-Sum diversification, monotone submodular functions and dynamic updates
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Dynamic diversification of continuous data
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Comprehension-based result snippets
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Diversifying query results on semi-structured data
Proceedings of the 21st ACM international conference on Information and knowledge management
DisC diversity: result diversification based on dissimilarity and coverage
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Top-k diversity queries over bounded regions
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Scalable diversification of multiple search results
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Diversified top-k graph pattern matching
Proceedings of the VLDB Endowment
POIKILO: a tool for evaluating the results of diversification models and algorithms
Proceedings of the VLDB Endowment
On the complexity of query result diversification
Proceedings of the VLDB Endowment
Efficient indexing for diverse query results
Proceedings of the VLDB Endowment
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In this paper we describe a general framework for evaluation and optimization of methods for diversifying query results. In these methods, an initial ranking candidate set produced by a query is used to construct a result set, where elements are ranked with respect to relevance and diversity features, i.e., the retrieved elements should be as relevant as possible to the query, and, at the same time, the result set should be as diverse as possible. While addressing relevance is relatively simple and has been heavily studied, diversity is a harder problem to solve. One major contribution of this paper is that, using the above framework, we adapt, implement and evaluate several existing methods for diversifying query results. We also propose two new approaches, namely the Greedy with Marginal Contribution (GMC) and the Greedy Randomized with Neighborhood Expansion (GNE) methods. Another major contribution of this paper is that we present the first thorough experimental evaluation of the various diversification techniques implemented in a common framework. We examine the methods' performance with respect to precision, running time and quality of the result. Our experimental results show that while the proposed methods have higher running times, they achieve precision very close to the optimal, while also providing the best result quality. While GMC is deterministic, the randomized approach (GNE) can achieve better result quality if the user is willing to tradeoff running time.