@inproceedings{b4f163227d724763ad81dde8957d48e8,
title = "Empirical evidence for search effectiveness models",
abstract = "Given a SERP in response to a user-originated query, Moffat et al. (CIKM 2013; TOIS 2017) suggest that C(i), the conditional continuation probability of the user examining the (i + 1) st element presented in the SERP, given that they are known to have examined the i th one, is positively correlated with both i and with the user's initial estimate of the volume of answer pages they are looking for, and negatively correlated with the extent to which suitable answer pages have been identified in the SERP at positions 1 through i. Here we first describe a methodology for specifying how C(i) should be defined in practical (as against ideal) settings, and then evaluate the applicability of the approach using three large search interaction logs from two different sources.",
keywords = "Adaptive metric, Average precision, Evaluation, User model",
author = "Wicaksono, {Alfan Farizki} and Alistair Moffat",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 27th ACM International Conference on Information and Knowledge Management, CIKM 2018 ; Conference date: 22-10-2018 Through 26-10-2018",
year = "2018",
month = oct,
day = "17",
doi = "10.1145/3269206.3269242",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "1571--1574",
editor = "Norman Paton and Selcuk Candan and Haixun Wang and James Allan and Rakesh Agrawal and Alexandros Labrinidis and Alfredo Cuzzocrea and Mohammed Zaki and Divesh Srivastava and Andrei Broder and Assaf Schuster",
booktitle = "CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management",
address = "United States",
}