SmartAd: A Smart System for Effective Advertising in Online Videos
Advertising in online videos is a large and growing market. In this paper, we propose a new approach to match ads with online videos based on the shopping interests of the target audience of videos. The proposed approach increases the relevance of ads to the actual viewers (humans) of videos, which increases the number of users who purchase goods and services offered by the advertisers. This in turn will increase the revenues for advertisers as well as the video sites as video sites usually charge advertisers based on the number of user clicks on their ads. The proposed approach is different from current approaches used in practice or proposed in the literatures, which most of them try to maximize the relevance of ads to the tags or contents of videos (objects). We conduct a subjective study to evaluate the performance of the proposed approach on many videos retrieved from YouTube. Our results show that the proposed approach yields more relevant ads to the viewers than the YouTube’s approach. We also compare against other approaches proposed in the literature and we show that the new approach outperforms them.
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On-going Research Problems
Estimating the click-through rate for new ads with semantic and feature based similarity algorithms
