A Framework for Spatial Feature Selection and Scoping and its Application to Geo-Targeting
Predicting if a particular user clicks on a particular ad is of critical importance for internet advertising. Associations between Internet ad performance data,such as number of clicks or Click Through Rate,CTR,and demographic data may be very weak on the global level ,but strong at the regional level.Identifying regions with strong associations of a continuous performance attribute with geo-features can create valuable knowledge for geo-targeted advertising. In this paper, we present a novel framework for in terestingness scoping to identify suchregions and discuss how such interestingness hotspots can be used for geo-feature eveluation with the goal to develop more accurate prediction models for advertisers. We also present the ZIPS algorithm that takes initial seed zip codes and discovers interestingness hotspots/coldspots, and a geo-feature pre-spatial data sets, combining data from a major ad network,demographic data from the 2000 Census, and binary feature data from other sources. Our experimental results demonstrate that creating geo-features can double CTRperformance for an Ad.
Spatial Data Mining Region discovery Geo-Feature Selection Contextual Advertising Behavioral Targeting
Ruth Miller ChunSheng Chen Christoph F.Eick Abraham Bagherjeiran
Department of Mathematics and Computer Science, University of Detroit Mercy 4001 W.McNichois Road,De Department of Computer Science, University of Houston 4800 Calhoun Rd,Houston,TX77004 USA ThinkersRUS sunnyvale,CA USA
国际会议
福州
英文
26-31
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）