document: AdHeat : an influence-based diffusion model for propagating hints to match ads In this paper , we present AdHeat , a 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 . We performed experiments on a large online Q&A community with half a million users . The experimental results show that AdHeat outperforms the relevance model on CTR ( click through rate ) by significant margins .
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