Comparing client and server dwell time estimates for click-level satisfaction prediction. Kim, Y., Hassan, A., White, R. W., & Zitouni, I. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, of SIGIR '14, pages 895–898, Gold Coast, Queensland, Australia, July, 2014. Association for Computing Machinery. ![Comparing client and server dwell time estimates for click-level satisfaction prediction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Click dwell time is the amount of time that a user spends on a clicked search result. Many previous studies have shown that click dwell time is strongly correlated with result-level satisfaction and document relevance. Accurate estimates of dwell time are therefore important for applications such as search satisfaction prediction and result ranking. However, dwell time can be estimated in different ways according to the information available about the search process. For example, a result reached for the query [Garfield] may involve 145s of "server-side" dwell time (observable to the search engine) and 40s of "client-side" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click and the next search event (click or query). Conversely, more detailed information about page dwell times can be obtained via client-side methods such as Web browser toolbars. The client-side information enables the estimation of more accurate dwell times by measuring the amount of time that a user spends on pages of interest (either the landing page, or pages on the full navigation trail). In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction. For this, we collect toolbar and search engine logs from real users, and provide an analysis of dwell times for improving prediction performance. Moreover, we show further improvements in predicting click-level satisfaction by combining dwell times with other query features (e.g., query clarity).
@inproceedings{kim_comparing_2014,
address = {Gold Coast, Queensland, Australia},
series = {{SIGIR} '14},
title = {Comparing client and server dwell time estimates for click-level satisfaction prediction},
isbn = {978-1-4503-2257-7},
url = {https://doi.org/10.1145/2600428.2609468},
doi = {10.1145/2600428.2609468},
abstract = {Click dwell time is the amount of time that a user spends on a clicked search result. Many previous studies have shown that click dwell time is strongly correlated with result-level satisfaction and document relevance. Accurate estimates of dwell time are therefore important for applications such as search satisfaction prediction and result ranking. However, dwell time can be estimated in different ways according to the information available about the search process. For example, a result reached for the query [Garfield] may involve 145s of "server-side" dwell time (observable to the search engine) and 40s of "client-side" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click and the next search event (click or query). Conversely, more detailed information about page dwell times can be obtained via client-side methods such as Web browser toolbars. The client-side information enables the estimation of more accurate dwell times by measuring the amount of time that a user spends on pages of interest (either the landing page, or pages on the full navigation trail). In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction. For this, we collect toolbar and search engine logs from real users, and provide an analysis of dwell times for improving prediction performance. Moreover, we show further improvements in predicting click-level satisfaction by combining dwell times with other query features (e.g., query clarity).},
urldate = {2020-05-12},
booktitle = {Proceedings of the 37th international {ACM} {SIGIR} conference on {Research} \& development in information retrieval},
publisher = {Association for Computing Machinery},
author = {Kim, Youngho and Hassan, Ahmed and White, Ryen W. and Zitouni, Imed},
month = jul,
year = {2014},
keywords = {click satisfaction, dwell time analysis},
pages = {895--898}
}
Downloads: 0
{"_id":"4n4ibezjQxo5rAGkw","bibbaseid":"kim-hassan-white-zitouni-comparingclientandserverdwelltimeestimatesforclicklevelsatisfactionprediction-2014","authorIDs":[],"author_short":["Kim, Y.","Hassan, A.","White, R. W.","Zitouni, I."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"Gold Coast, Queensland, Australia","series":"SIGIR '14","title":"Comparing client and server dwell time estimates for click-level satisfaction prediction","isbn":"978-1-4503-2257-7","url":"https://doi.org/10.1145/2600428.2609468","doi":"10.1145/2600428.2609468","abstract":"Click dwell time is the amount of time that a user spends on a clicked search result. Many previous studies have shown that click dwell time is strongly correlated with result-level satisfaction and document relevance. Accurate estimates of dwell time are therefore important for applications such as search satisfaction prediction and result ranking. However, dwell time can be estimated in different ways according to the information available about the search process. For example, a result reached for the query [Garfield] may involve 145s of \"server-side\" dwell time (observable to the search engine) and 40s of \"client-side\" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click and the next search event (click or query). Conversely, more detailed information about page dwell times can be obtained via client-side methods such as Web browser toolbars. The client-side information enables the estimation of more accurate dwell times by measuring the amount of time that a user spends on pages of interest (either the landing page, or pages on the full navigation trail). In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction. For this, we collect toolbar and search engine logs from real users, and provide an analysis of dwell times for improving prediction performance. Moreover, we show further improvements in predicting click-level satisfaction by combining dwell times with other query features (e.g., query clarity).","urldate":"2020-05-12","booktitle":"Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval","publisher":"Association for Computing Machinery","author":[{"propositions":[],"lastnames":["Kim"],"firstnames":["Youngho"],"suffixes":[]},{"propositions":[],"lastnames":["Hassan"],"firstnames":["Ahmed"],"suffixes":[]},{"propositions":[],"lastnames":["White"],"firstnames":["Ryen","W."],"suffixes":[]},{"propositions":[],"lastnames":["Zitouni"],"firstnames":["Imed"],"suffixes":[]}],"month":"July","year":"2014","keywords":"click satisfaction, dwell time analysis","pages":"895–898","bibtex":"@inproceedings{kim_comparing_2014,\n\taddress = {Gold Coast, Queensland, Australia},\n\tseries = {{SIGIR} '14},\n\ttitle = {Comparing client and server dwell time estimates for click-level satisfaction prediction},\n\tisbn = {978-1-4503-2257-7},\n\turl = {https://doi.org/10.1145/2600428.2609468},\n\tdoi = {10.1145/2600428.2609468},\n\tabstract = {Click dwell time is the amount of time that a user spends on a clicked search result. Many previous studies have shown that click dwell time is strongly correlated with result-level satisfaction and document relevance. Accurate estimates of dwell time are therefore important for applications such as search satisfaction prediction and result ranking. However, dwell time can be estimated in different ways according to the information available about the search process. For example, a result reached for the query [Garfield] may involve 145s of \"server-side\" dwell time (observable to the search engine) and 40s of \"client-side\" dwell time (observable from the browser). Since search engines can only observe server-side actions (i.e., activity on the search engine result page), server-side dwell times are estimated by measuring the time between a search result click and the next search event (click or query). Conversely, more detailed information about page dwell times can be obtained via client-side methods such as Web browser toolbars. The client-side information enables the estimation of more accurate dwell times by measuring the amount of time that a user spends on pages of interest (either the landing page, or pages on the full navigation trail). In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction. For this, we collect toolbar and search engine logs from real users, and provide an analysis of dwell times for improving prediction performance. Moreover, we show further improvements in predicting click-level satisfaction by combining dwell times with other query features (e.g., query clarity).},\n\turldate = {2020-05-12},\n\tbooktitle = {Proceedings of the 37th international {ACM} {SIGIR} conference on {Research} \\& development in information retrieval},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {Kim, Youngho and Hassan, Ahmed and White, Ryen W. and Zitouni, Imed},\n\tmonth = jul,\n\tyear = {2014},\n\tkeywords = {click satisfaction, dwell time analysis},\n\tpages = {895--898}\n}\n\n","author_short":["Kim, Y.","Hassan, A.","White, R. W.","Zitouni, I."],"key":"kim_comparing_2014","id":"kim_comparing_2014","bibbaseid":"kim-hassan-white-zitouni-comparingclientandserverdwelltimeestimatesforclicklevelsatisfactionprediction-2014","role":"author","urls":{"Paper":"https://doi.org/10.1145/2600428.2609468"},"keyword":["click satisfaction","dwell time analysis"],"downloads":0},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/jsan","creationDate":"2020-05-26T09:25:38.308Z","downloads":0,"keywords":["click satisfaction","dwell time analysis"],"search_terms":["comparing","client","server","dwell","time","estimates","click","level","satisfaction","prediction","kim","hassan","white","zitouni"],"title":"Comparing client and server dwell time estimates for click-level satisfaction prediction","year":2014,"dataSources":["h8ZDyzMApwwGDDKcf"]}