Predictive models and analysis for webpage depth-level dwell time

Chong Wang, Shuai Zhao, Achir Kalra, Cristian Borcea, Yi Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A half of online display ads are not rendered viewable because the users do not scroll deep enough or spend sufficient time at the page depth where the ads are placed. In order to increase the marketing efficiency and ad effectiveness, there is a strong demand for viewability prediction from both advertisers and publishers. This paper aims to predict the dwell time for a given ‹user, page, depth› triplet based on historic data collected by publishers. This problem is difficult because of user behavior variability and data sparsity. To solve it, we propose predictive models based on Factorization Machines and Field-aware Factorization Machines in order to overcome the data sparsity issue and provide flexibility to add auxiliary information such as the visible area of a user's browser. In addition, we leverage the prior dwell time behavior of the user within the current page view, that is, time series information, to further improve the proposed models. Experimental results using data from a large web publisher demonstrate that the proposed models outperform comparison models. Also, the results show that adding time series information further improves the performance.

Original languageEnglish (US)
Pages (from-to)1007-1022
Number of pages16
JournalJournal of the Association for Information Science and Technology
Volume69
Issue number8
DOIs
StatePublished - Aug 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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