Abstract
In this paper, we present a novel Integer Linear Programming formulation (termed ILP4ID) for implicit search result diversification (SRD). The advantage is that the exact solution can be achieved, which enables us to investigate to what extent using the greedy strategy affects the performance of implicit SRD. Specifically, a series of experiments are conducted to empirically compare the state-of-the-art methods with the proposed approach. The experimental results show that: (1) The factors, such as different initial runs and the number of input documents, greatly affect the performance of diversification models. (2) ILP4ID can achieve substantially improved performance over the state-of-the-art methods in terms of standard diversity metrics.
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References
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st SIGIR, pp. 335–336 (1998)
Sanner, S., Guo, S., Graepel, T., Kharazmi, S., Karimi, S.: Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model. In: Proceedings of the 20th CIKM, pp. 1977–1980 (2011)
Santos, R.L.T., Macdonald, C., Ounis, I.: Search result diversification. Found. Trends Inf. Retr. 9(1), 1–90 (2015)
Wang, J., Zhu, J.: Portfolio theory of information retrieval. In: Proceedings of the 32nd SIGIR, pp. 115–122 (2009)
Yu, H., Ren, F.: Search result diversification via filling up multiple knapsacks. In: Proceedings of the 23rd CIKM, pp. 609–618 (2014)
Zuccon, G., Azzopardi, L., Zhang, D., Wang, J.: Top-k retrieval using facility location analysis. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 305–316. Springer, Heidelberg (2012)
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Yu, HT. et al. (2016). An In-Depth Study of Implicit Search Result Diversification. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_29
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DOI: https://doi.org/10.1007/978-3-319-48051-0_29
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