Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual advertising by combining relevance with click feedback
Proceedings of the 17th international conference on World Wide Web
Multimedia features for click prediction of new ads in display advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Display advertising has been a significant source of revenue for publishers and ad networks in the online advertising ecosystem. One of the main goals in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Although %in the online advertising industry we believe that the visual appearance of ads (creatives) matters for propensity of user response, there is no published work so far to address this topic via a systematic data-driven approach. In this paper we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and some are inspired by related work. We extracted these features from real creatives served on RightMedia. Then, we present recommendations of visual features that have the most important impact on CTR to the professional designers in order to optimize their creative design. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. We have also designed and conducted an experiment to evaluate the effectiveness of visual features by themselves for CTR prediction.