Abstract
In this paper a method for aggregating Web search results is proposed. The aggregator results are compared with the results of most popular search engines: Google, Bing and Yandex. There are 3 stages of the comparison, one for each of the languages: English, Polish and Russian. The quality of the aggregator search results is tested based on user preferences and measured with normalized discounted cumulative gain (nDCG).
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References
Brorsson, M., Lindhom, H.: The best place to hide a dead body is page 2 on Google search results (2016)
Colgrove, C., Martin, G., Campanini, J.: Simple web search. U.S. Patent 8,868,537, 21 October 2014
Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice, vol. 283. Addison-Wesley Reading, Boston (2010)
Glover, E.J., Lawrence, S., Birmingham, W.P., Giles, C.L.: Architecture of a metasearch engine that supports user information needs. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 210–216. ACM (1999)
Ishii, H., Tempo, R.: The PageRank problem, multiagent consensus, and web aggregation: a systems and control viewpoint. IEEE Control Syst. 34(3), 34–53 (2014)
Jansen, B.J., Spink, A.: Investigating customer click through behaviour with integrated sponsored and nonsponsored results. Int. J. Internet Mark. Advertising 5(1), 74 (2009)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)
Newman, E., Lockett, J.: Dynamic aggregation and display of contextually relevant content. U.S. Patent 7,917,840, 29 March 2011
Patel, B., Shah, D.: Ranking algorithm for meta search engine. IJAERS Int. J. Adv. Eng. Res. Stud. 2(1), 39–40 (2012)
Sherkhonov, E., Cuenca Grau, B., Kharlamov, E., Kostylev, E.V.: Semantic faceted search with aggregation and recursion. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 594–610. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_35
Wang, Y., Wang, L., Li, Y., He, D., Chen, W., Liu, T.Y.: A theoretical analysis of NDCG ranking measures. In: Proceedings of the 26th Annual Conference on Learning Theory (COLT 2013), vol. 8 (2013)
Yun, J.M., He, Y., Elnikety, S., Ren, S.: Optimal aggregation policy for reducing tail latency of web search. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 63–72. ACM (2015)
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Kopel, M., Buben, M. (2019). Aggregating Web Search Results. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_16
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DOI: https://doi.org/10.1007/978-3-030-14799-0_16
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