Revenue-Optimized Webpage Recommendation

Chong Wang, Achir Kalra, Cristian Borcea, Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

As a massive industry, display advertising delivers advertisers' marketing messages to attract customers throughbanners shown on webpages. For publishers, i.e. websites, display advertising is the most critical revenue source. Most existing webpage recommender systems suggest webpages based on user interests only. However, the articles of interest to specific users may not be profitable to publishers. Conversely, only recommending the most profitable articles may lose publishers' user base. To address this issue, we will conduct a series of investigations anddesign Revenue-Optimized Recommendation, aims to recommend users webpages that optimize interestingness and ad revenue.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
EditorsXindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1558-1559
Number of pages2
ISBN (Electronic)9781467384926
DOIs
StatePublished - Jan 29 2016
Event15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Other

Other15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications

Keywords

  • Computational Advertising
  • Recommender System

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