The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Diversifying search results
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.
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Citations
Cites
Introduction to information retrieval
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Cited by
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
“Likewise, certain AI problems (such as content recommendation or information retrieval) also aim at producing diverse recommendations rather than highly-scoring yet similar results [405,406] .”
Test Collection Based Evaluation of Information Retrieval Systems
Sanderson, M. (2010) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000009 · Cited by 485
Large Language Models for Information Retrieval: A Survey
Zhu, Y., Yuan, H., Wang, S., Liu, J., Liu, W., Deng, C., Chen, H., Liu, Z., Dou, Z., & Wen, J.-R. (2026) · ACM Transactions on Information Systems · doi:10.1145/3748304 · Cited by 225
Kabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms · doi:10.3390/a18090556 · Cited by 44
FAIR : Fairness‐aware information retrieval evaluation
Gao, R., Ge, Y., & Shah, C. (2022) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24648 · Cited by 19
Detecting duplicate web documents using clickthrough data
Radlinski, F., Bennett, P. N., & Yilmaz, E. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935859 · Cited by 19
Coverage-based search result diversification
Zheng, W., Wang, X., Fang, H., & Cheng, H. (2012) · Information Retrieval · doi:10.1007/s10791-011-9178-4 · Cited by 16
Hybrid entity clustering using crowds and data
Lee, J., Cho, H., Park, J.-W., Cha, Y.-R., Hwang, S.-W., Nie, Z., & Wen, J.-R. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0328-8 · Cited by 15
Metadata
- Authors
- Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S.
- Venue
- Proceedings of the Second ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2009-02-09
- Pages
- 5-14
- OpenAlex
- W1993320088
- Cited by
- 1,020
- References
- 1
- FWCI
- 68.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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