Revenue-Optimized Webpage Recommendation
Document Type
Conference Proceeding
Publication Date
1-29-2016
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.
Identifier
84964797350 (Scopus)
ISBN
[9781467384926]
Publication Title
Proceedings 15th IEEE International Conference on Data Mining Workshop Icdmw 2015
External Full Text Location
https://doi.org/10.1109/ICDMW.2015.215
First Page
1558
Last Page
1559
Grant
CNS 1409523
Fund Ref
National Science Foundation
Recommended Citation
Wang, Chong; Kalra, Achir; Borcea, Cristian; and Chen, Yi, "Revenue-Optimized Webpage Recommendation" (2016). Faculty Publications. 10705.
https://digitalcommons.njit.edu/fac_pubs/10705
