Predictive models and analysis for webpage depth-level dwell time

Document Type

Article

Publication Date

8-1-2018

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.

Identifier

85047477511 (Scopus)

Publication Title

Journal of the Association for Information Science and Technology

External Full Text Location

https://doi.org/10.1002/asi.24025

e-ISSN

23301643

ISSN

23301635

First Page

1007

Last Page

1022

Issue

8

Volume

69

Grant

CNS 1409523

Fund Ref

National Science Foundation

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