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Webpage Depth-level Dwell Time Prediction

Authors:

Chong Wang,

Achir Kalra,

Cristian Borcea,

Yi ChenAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1937 - 1940

https://doi.org/10.1145/2983323.2983878

Published: 24 October 2016 Publication History

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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.

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Cited By

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Sarkar SShah CChua TLauw HSi LTerzi ETsaparas P(2023)A Synthetic Search Session Generator for Task-Aware Information Seeking and RetrievalProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3573041(1156-1159)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3573041
Makkar AGhosh USharma PJaved A(2023)A Fuzzy-Based Approach to Enhance Cyber Defence Security for Next-Generation IoTIEEE Internet of Things Journal10.1109/JIOT.2021.305332610:3(2079-2086)Online publication date: 1-Feb-2023
https://doi.org/10.1109/JIOT.2021.3053326
Asad MHalim ZWaqas MTu S(2021)An In-ad contents-based viewability prediction framework using Artificial Intelligence for Web AdsArtificial Intelligence Review10.1007/s10462-021-10013-3Online publication date: 10-May-2021
https://doi.org/10.1007/s10462-021-10013-3
Show More Cited By

Index Terms

Webpage Depth-level Dwell Time Prediction
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

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Short-paper

Funding Sources

an endowment from the Leir Charitable Foundations
Google Research Award
National Science Foundation

Conference

CIKM'16

Sponsor:

CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Sarkar SShah CChua TLauw HSi LTerzi ETsaparas P(2023)A Synthetic Search Session Generator for Task-Aware Information Seeking and RetrievalProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3573041(1156-1159)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3573041
Makkar AGhosh USharma PJaved A(2023)A Fuzzy-Based Approach to Enhance Cyber Defence Security for Next-Generation IoTIEEE Internet of Things Journal10.1109/JIOT.2021.305332610:3(2079-2086)Online publication date: 1-Feb-2023
https://doi.org/10.1109/JIOT.2021.3053326
Asad MHalim ZWaqas MTu S(2021)An In-ad contents-based viewability prediction framework using Artificial Intelligence for Web AdsArtificial Intelligence Review10.1007/s10462-021-10013-3Online publication date: 10-May-2021
https://doi.org/10.1007/s10462-021-10013-3
Schler JHammer A(2021)Calibrated Viewability Prediction for Premium Inventory ExpansionInformation Management and Big Data10.1007/978-3-030-76228-5_29(403-418)Online publication date: 12-May-2021
https://doi.org/10.1007/978-3-030-76228-5_29
Wang CZhao SKalra ABorcea CChen Y(2019)Webpage Depth Viewability Prediction Using Deep Sequential Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.283959931:3(601-614)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2839599
Yoshida AHigurashi TMaruishi MTateiwa NHata NTanaka AWakamatsu TNagamatsu KTajima AFujisawa K(2019)New Performance Index “Attractiveness Factor” for Evaluating Websites via Obtaining Transition of Users’ InterestsData Science and Engineering10.1007/s41019-019-00112-15:1(48-64)Online publication date: 21-Nov-2019
https://doi.org/10.1007/s41019-019-00112-1
Ackerman BWang CChen Y(2018)A session‐specific opportunity cost model for rank‐oriented recommendationJournal of the Association for Information Science and Technology10.1002/asi.2404469:10(1259-1270)Online publication date: 7-Aug-2018
https://doi.org/10.1002/asi.24044
Wang CZhao SKalra ABorcea CChen Y(2018)Predictive models and analysis for webpage depth‐level dwell timeJournal of the Association for Information Science and Technology10.1002/asi.2402569:8(1007-1022)Online publication date: 20-May-2018
https://doi.org/10.1002/asi.24025

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