View source for 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.
== People ==

* [http://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]

* [http://www.cs.sfu.ca/~hsadeghi/ Hamed Sadeghi Neshat (MSc student)]



== On-going Research Problems == 

Estimating the click-through rate for new ads with semantic and feature based similarity algorithms




== References and Links  ==