Adslot Mining for Online Display Ads.

Finding appropriate adslots to display ads is an important step to achieve high conversion rates in online display advertising. Previous work on ad recommendation and conversion prediction often focuses on matching between adslots, users and ads simultaneously for each impression at micro level. Such methods require rich attributes of users, ads and adslots, which might not always be available, especially with ad-adslot pairs that have never been displayed. In this research, we propose a macro approach for mining new adslots for each ad by recommending appropriate adslots to the ad. The proposed method does not require any user information and can be pre-calculated offline, even when there are not any impressions of the ad on the target adslots. It applies matrix factorization techniques to the ad-adslot performance history matrix to calculate the predicted performance of the target adslots. Experiments show that the proposed method achieves a small root mean-square error (RMSE) when testing with offline data and it yields high conversion rates in online tests with real-world ad campaigns.