Learning To Rank Audience For Behavioral Targeting

Behavioral Targeting (BT) is a recent trend of online advertising market. However, some classical BT solutions, which predefine the user segments for BT ads delivery, are sometimes too large to numerous long-tail advertisers, who cannot afford to buy any large user segments due to budget consideration. In this extend abstract, we propose to rank users according to their probability of interest in an advertisement in a learning to rank framework. We propose to extract three types of features between user behaviors such as search queries, ad click history etc and the ad content provided by advertisers. Through this way, a long-tail advertiser can select a certain number of top ranked users as needed from the user segments for ads delivery. In the experiments, we use a 30-days' ad click-through log from a commercial search engine. The results show that using our proposed features under a learning to rank framework, we can well rank users who potentially interest in an advertisement.