Abstract
User effort is an important measurement of search quality. It strongly affects user experience and finally, affects conversion. There are measurements about user effort in search. However, they all take only query result browsing efforts into account. Few of them measures the effort of query selection, or the effort to choose a suitable query. This paper shows that query selection effort is a significant part of overall user effort, almost as important as browsing effort. This paper further introduces an entropy-like effort measurement approach for query selection. Statistic and simulation results strongly indicate that our measurement reflects real user effort better.
This paper is supported by National Nature Science Foundation of China (61562020, 61862021) and Hainan Provincial Natural Science Foundation of China (618QN217).
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References
Arampatzis, A., Kamps, J.: A study of query length. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 811–812. SIGIR 2008, ACM, New York (2008). https://doi.org/10.1145/1390334.1390517, https://doi.acm.org/10.1145/1390334.1390517
Azzopardi, L.: Query side evaluation: an empirical analysis of effectiveness and effort. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2009, pp. 556–563. ACM, New York (2009). https://doi.org/10.1145/1571941.1572037, https://doi.acm.org/10.1145/1571941.1572037
Cancho, R.F.I., Solé, R.V.: Least effort and the origins of scaling in human language. Proc. Nat. Acad. Sci. 100(3), 788–791 (2003). https://doi.org/10.1073/pnas.0335980100. https://www.pnas.org/content/100/3/788
Elman, J.L.: An alternative view of the mental lexicon. In: Trends in Cognitive Sciences, pp. 301–306 (2004)
Fay, D., Cutler, A.: Malapropisms and the structure of the mental lexicon. Linguist. Inquiry 8(3), 505–520 (1977). http://www.jstor.org/stable/4177997
Ferro, N., Silvello, G., Keskustalo, H., Pirkola, A., Järvelin, K.: The twist measure for IR evaluation: taking user’s effort into account. JASIST 67, 620–648 (2016)
Kempen, G., Vosse, T.: Incremental syntactic tree formation in human sentence processing, a cognitive architecture based on activation decay and simulated annealing. In: Sharkey, N. (ed.) Connectionist Natural Language Processing: Readings from Connection Science, pp. 83–100. Springer, Dordrecht (1992). https://doi.org/10.1007/978-94-011-2624-3_5
Long, C., Wong, R.C.W., Wei, V.J.: Profit maximization with sufficient customer satisfactions. ACM Trans. Knowl. Discov. Data 12(2), 19:1–19:34 (2018). https://doi.org/10.1145/3110216. https://doi.acm.org/10.1145/3110216
Lv, Y.: A study of query length heuristics in information retrieval. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. CIKM 2015, pp. 1747–1750. ACM, New York (2015). https://doi.org/10.1145/2806416.2806592, https://doi.acm.org/10.1145/2806416.2806592
Petersen, C., Simonsen, J.G., Lioma, C.: Power law distributions in information retrieval. ACM Trans. Inf. Syst. 34(2), 8:1–8:37 (2016). https://doi.org/10.1145/2816815. https://doi.acm.org/10.1145/2816815
Peterson, J., Dixit, P.D., Dill, K.A.: A maximum entropy framework for nonexponential distributions. Proc. Nat. Acad. Sci. 110(51), 20380–20385 (2013). https://doi.org/10.1073/pnas.1320578110. https://www.pnas.org/content/110/51/20380
de Vries, A.P., Kazai, G., Lalmas, M.: Tolerance to irrelevance: a user-effort oriented evaluation of retrieval systems without predefined retrieval unit. In: Coupling Approaches, Coupling Media and Coupling Languages for Information Retrieval, pp. 463–473. RIAO 2004, Le Centre de Hautes Etudes Internationales D’Informatique Documentaire, Paris, France, France (2004). http://dl.acm.org/citation.cfm?id=2816272.2816314
Wu, P., Wen, J.R., Liu, H., Ma, W.Y.: Query selection techniques for efficient crawling of structured web sources. In: Proceedings of the 22nd International Conference on Data Engineering. ICDE 2006, p. 47. IEEE Computer Society, Washington, DC (2006). https://doi.org/10.1109/ICDE.2006.124
Yilmaz, E., Verma, M., Craswell, N., Radlinski, F., Bailey, P.: Relevance and effort: an analysis of document utility. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. CIKM 2014, pp. 91–100. ACM, New York (2014). https://doi.org/10.1145/2661829.2661953
Youn, H., et al.: On the universal structure of human lexical semantics. Proc. Nat. Acad. Sci. 113(7), 1766–1771 (2016). https://doi.org/10.1073/pnas.1520752113. https://www.pnas.org/content/113/7/1766
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Yu, S., Jin, T., Shi, Z., Li, J., Pan, J. (2019). A User Effort Measurement for Query Selection. In: Zhang, Q., Liao, X., Ren, Z. (eds) Information Retrieval. CCIR 2019. Lecture Notes in Computer Science(), vol 11772. Springer, Cham. https://doi.org/10.1007/978-3-030-31624-2_2
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