Webpage depth-level dwell time prediction

Chong Wang; Achir Kalra; Cristian Borcea; Yi Chen

doi:10.1145/2983323.2983878

Webpage depth-level dwell time prediction

Chong Wang, Achir Kalra, Cristian Borcea, Yi Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given <user, webpage, depth> triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.

Original language	English (US)
Title of host publication	CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	1937-1940
Number of pages	4
ISBN (Electronic)	9781450340731
DOIs	https://doi.org/10.1145/2983323.2983878
State	Published - Oct 24 2016
Event	25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: Oct 24 2016 → Oct 28 2016

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
Volume	24-28-October-2016

Other

Other	25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/Territory	United States
City	Indianapolis
Period	10/24/16 → 10/28/16

All Science Journal Classification (ASJC) codes

General Decision Sciences
General Business, Management and Accounting

Keywords

Computational advertising
Data mining
User behavior

Access to Document

10.1145/2983323.2983878

Cite this

Wang, C., Kalra, A., Borcea, C., & Chen, Y. (2016). Webpage depth-level dwell time prediction. In CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management (pp. 1937-1940). (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016). Association for Computing Machinery. https://doi.org/10.1145/2983323.2983878

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title = "Webpage depth-level dwell time prediction",

abstract = "The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.",

keywords = "Computational advertising, Data mining, User behavior",

author = "Chong Wang and Achir Kalra and Cristian Borcea and Yi Chen",

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doi = "10.1145/2983323.2983878",

language = "English (US)",

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Wang, C, Kalra, A, Borcea, C & Chen, Y 2016, Webpage depth-level dwell time prediction. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, vol. 24-28-October-2016, Association for Computing Machinery, pp. 1937-1940, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 10/24/16. https://doi.org/10.1145/2983323.2983878

Webpage depth-level dwell time prediction. / Wang, Chong; Kalra, Achir; Borcea, Cristian et al.
CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. Association for Computing Machinery, 2016. p. 1937-1940 (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Webpage depth-level dwell time prediction

AU - Wang, Chong

AU - Kalra, Achir

AU - Borcea, Cristian

AU - Chen, Yi

PY - 2016/10/24

Y1 - 2016/10/24

N2 - The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.

AB - The amount of time spent by users at specific page depths within webpages, called dwell time, can be used by web publishers to decide where to place online ads and what type of ads to place at different depths within a webpage. This paper presents a model to predict the dwell time for a given triplet based on historic data collected by publishers. Dwell time prediction is difficult due to user behavior variability and data sparsity. We adopt the Factorization Machines model because it is able to capture the interaction between users and webpages, overcome the data sparsity issue, and provide flexibility to add auxiliary information such as the visible area of a user's browser. Experimental results using data from a large web publisher demonstrate that our model outperforms deterministic and regression-based comparison models.

KW - Computational advertising

KW - Data mining

KW - User behavior

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M3 - Conference contribution

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T3 - International Conference on Information and Knowledge Management, Proceedings

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ER -

Webpage depth-level dwell time prediction

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All Science Journal Classification (ASJC) codes

Keywords

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