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Predictive models and analysis for webpage depth‐level dwell time
Author(s) -
Wang Chong,
Zhao Shuai,
Kalra Achir,
Borcea Cristian,
Chen Yi
Publication year - 2018
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24025
Subject(s) - computer science , dwell time , leverage (statistics) , display advertising , scroll , web page , flexibility (engineering) , factorization , field (mathematics) , data mining , information retrieval , online advertising , machine learning , the internet , world wide web , algorithm , medicine , clinical psychology , statistics , mathematics , archaeology , history , pure mathematics
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 〈 u s e r , p a g e , d e p t h 〉 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.