TY - CONF TI - On Query Result Diversification AU - Vieira, Marcos R. AU - Razente, Humberto L. AU - Barioni, Maria C. N. AU - Hadjieleftheriou, Marios AU - Srivastava, Divesh AU - Traina, Caetano AU - Tsotras, Vassilis J. T3 - ICDE '11 AB - 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. C1 - Washington, DC, USA C3 - Proceedings of the 2011 IEEE 27th International Conference on Data Engineering DA - 2011/// PY - 2011 DO - 10.1109/ICDE.2011.5767846 DP - ACM Digital Library SP - 1163 EP - 1174 LA - en PB - IEEE Computer Society SN - 978-1-4244-8959-6 UR - http://dx.doi.org/10.1109/ICDE.2011.5767846 Y2 - 2019/01/27/22:10:26 ER - TY - CONF TI - Diversifying Search Results AU - Agrawal, Rakesh AU - Gollapudi, Sreenivas AU - Halverson, Alan AU - Ieong, Samuel T3 - WSDM '09 AB - We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines. C1 - New York, NY, USA C3 - Proceedings of the Second ACM International Conference on Web Search and Data Mining DA - 2009/// PY - 2009 DO - 10.1145/1498759.1498766 DP - ACM Digital Library SP - 5 EP - 14 LA - en PB - ACM SN - 978-1-60558-390-7 UR - http://doi.acm.org/10.1145/1498759.1498766 Y2 - 2019/01/27/21:41:12 ER -