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Vieira, M. R., Razente, H. L., Barioni, M. C. N., Hadjieleftheriou, M., Srivastava, D., Traina, C., & Tsotras, V. J. (2011). On Query Result Diversification. In Proceedings of the 2011 IEEE 27th International Conference on Data Engineering (pp. 1163–1174). Washington, DC, USA: IEEE Computer Society. https://doi.org/10.1109/ICDE.2011.5767846
Robertson, S., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends® in Information Retrieval, 3(4), 333–389. https://doi.org/10.1561/1500000019
Agrawal, R., Gollapudi, S., Halverson, A., & Ieong, S. (2009). Diversifying Search Results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining (pp. 5–14). New York, NY, USA: ACM. https://doi.org/10.1145/1498759.1498766
Joachims, T. (2002). Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 133–142). Edmonton, Alberta, Canada: ACM. https://doi.org/10.1145/775047.775067
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Salton, G., Wong, A., & Yang, C. S. (1975). A Vector Space Model for Automatic Indexing. Commun. ACM, 18(11), 613–620. https://doi.org/10.1145/361219.361220
Maron, M. E., & Kuhns, J. L. (1960). On Relevance, Probabilistic Indexing and Information Retrieval. Journal of the ACM, 7(3), 216–244. https://doi.org/10.1145/321033.321035

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