Abstract
k-nearest neighbor (k-NN) queries are well-known and widely used in a plethora of applications. In the original definition of k-NN queries there is no concern regarding diversity of the answer set, even though in some scenarios it may be interesting. For instance, travelers may be looking for touristic sites that are not too far from where they are but that would help them seeing different parts of the city. Likewise, if one is looking for restaurants close by, it may be more interesting to return restaurants of different categories or ethnicities which are nonetheless relatively close. The interesting novel aspect of this type of query is that there are competing criteria to be optimized. We propose two approaches that leverage the notion of linear skyline queries in order to find spatially- and category-wise diverse k-NNs w.r.t. a given query point and which return all optimal solutions for any linear combination of the weights a user could give to the two competing criteria. Our experiments, varying a number of parameters, show that our approaches are several orders of magnitude faster than a straightforward approach.
This research has been partially supported by NSERC, Canada and CNPq’s Science Without Borders program, Brazil.
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Notes
- 1.
For simplicity we assume that CS is the set of |CS|-NN wrt the query point.
References
Abbar, S., et al.: Diverse near neighbor problem. In: SOCG, pp. 207–214 (2013)
Börzsönyi, S., et al.: The skyline operator. In: ICDE, pp. 421–430 (2001)
Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)
Carterette, B.: An analysis of NP-completeness in novelty and diversity ranking. Inf. Retrieval 14, 89–106 (2011)
Clarke, C.L., et al.: Novelty and diversity in information retrieval evaluation. In: SIGIR, pp. 659–666 (2008)
Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW, pp. 381–390 (2009)
Gu, Y., et al.: The moving k diversified nearest neighbor query. In: TKDE, pp. 2778–2792 (2016)
Handl, J., Knowles, J.: Cluster generators for large high-dimensional data sets with large numbers of clusters (2005). http://dbkgroup.org/handl/generators
Huang, Z., et al.: A clustering based approach for skyline diversity. Expert Syst. Appl. 38(7), 7984–7993 (2011)
Jain, A., Sarda, P., Haritsa, J.R.: Providing diversity in K-nearest neighbor query results. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 404–413. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24775-3_49
Kucuktunc, O., Ferhatosmanoglu, H.: \(\lambda \)-diverse nearest neighbors browsing for multidimensional data. In: TKDE, pp. 481–493 (2013)
Lee, K.C.K., Lee, W.-C., Leong, H.V.: Nearest surrounder queries. In: ICDE, p. 85 (2006)
Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: WWW, pp. 781–790 (2010)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. ACM SIGMOD Rec. 24, 71–79 (1995)
Shekelyan, M., Jossé, G., Schubert, M., Kriegel, H.-P.: Linear path skyline computation in bicriteria networks. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 173–187. Springer, Cham (2014). doi:10.1007/978-3-319-05810-8_12
Tao, Y.: Diversity in skylines. IEEE Data Eng. Bull. 32(4), 65–72 (2009)
Valkanas, G., Papadopoulos, A.N., Gunopulos, D.: Skydiver: a framework for skyline diversification. In: EDBT, pp. 406–417 (2013)
Vieira, M.R., et al.: On query result diversification. In: ICDE, pp. 1163–1174 (2011)
Yu, C., Lakshmanan, L., and Amer-Yahia, S.: It takes variety to make a world: diversification in recommender systems. In: EDBT 2009, pp. 368–378 (2009)
Zhang, C., et al.: Diversified spatial keyword search on road networks. In: EDBT, pp. 367–378 (2014)
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F. Costa, C., Nascimento, M.A. (2017). Towards Spatially- and Category-Wise k-Diverse Nearest Neighbors Queries. In: Gertz, M., et al. Advances in Spatial and Temporal Databases. SSTD 2017. Lecture Notes in Computer Science(), vol 10411. Springer, Cham. https://doi.org/10.1007/978-3-319-64367-0_9
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