Abstract
The key issue in top-k retrieval — finding a set of k documents (from a large document collection) that can best answer a user’s query — is to strike the optimal balance between relevance and diversity.
In this paper, we study the top-k retrieval problem in the framework of facility location analysis and prove the submodularity of that objective function which provides a theoretical approximation guarantee of factor \(1 - \frac{1}{e}\) for the (best-first) greedy search algorithm. Furthermore, we propose a two-stage hybrid search strategy which first obtains a high-quality initial set of top-k documents via greedy search, and then refines that result set iteratively via local search.
Experiments on two large TREC benchmark datasets show that our two-stage hybrid search strategy approach outperforms the existing ones.
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Sha, C., Wang, K., Zhang, D., Wang, X., Zhou, A. (2014). Optimizing Top-k Retrieval: Submodularity Analysis and Search Strategies. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_3
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DOI: https://doi.org/10.1007/978-3-319-08010-9_3
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