Diversifying search results Export

@inproceedings{citeulike:4123592, abstract = {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.}, address = {New York, NY, USA}, author = {Agrawal, Rakesh and Gollapudi, Sreenivas and Halverson, Alan and Ieong, Samuel}, booktitle = {WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining}, citeulike-article-id = {4123592}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1498759.1498766}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1498759.1498766}, doi = {10.1145/1498759.1498766}, isbn = {978-1-60558-390-7}, keywords = {diversification, search\_engines}, location = {Barcelona, Spain}, pages = {5--14}, posted-at = {2009-10-26 16:56:48}, priority = {2}, publisher = {ACM}, title = {Diversifying search results}, url = {http://dx.doi.org/10.1145/1498759.1498766}, year = {2009} }

TY - CONF ID - citeulike:4123592 L3 - citeulike-article-id:4123592 N2 - 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. CY - Barcelona, Spain SN - 978-1-60558-390-7 AD - New York, NY, USA EP - 14 TI - Diversifying search results PB - ACM T2 - WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining SP - 5 KW - diversification KW - search_engines AU - Agrawal, Rakesh AU - Gollapudi, Sreenivas AU - Halverson, Alan AU - Ieong, Samuel PY - 2009/// UR - http://dx.doi.org/10.1145/1498759.1498766 ER -