| Hi-index | 0.00 |
AdHeat is our newly developed social ad model considering user influence in addition to relevance for matching ads Traditionally, ad placement employs the relevance model Such a model matches ads with Web page content, user interests, or both We have observed, however, on social networks that the relevance model suffers from two shortcomings First, influential users (users who contribute opinions) seldom click ads that are highly relevant to their expertise Second, because influential users' contents and activities are attractive to other users, hint words summarizing their expertise and activities may be widely preferred Therefore, we propose AdHeat, which diffuses hint words of influential users to others and then matches ads for each user with aggregated hints Our experimental results on a large-scale social network show that AdHeat outperforms the relevance model on CTR (click through rate) by significant margins In this talk, the algorithms employed by AdHeat and solutions to address scalability issues are presented.