Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
This work addresses the need for more accurate user behavior targeting in online advertising, offering a novel approach that is incremental in improving ad performance.
The paper tackles the problem of predicting ad click-through rates by leveraging user online browsing similarity, proposing a transfer learning framework that transfers knowledge from browsing behavior to ad response prediction, achieving significant improvement over strong baselines in large-scale experiments.
User behaviour targeting is essential in online advertising. Compared with sponsored search keyword targeting and contextual advertising page content targeting, user behaviour targeting builds users' interest profiles via tracking their online behaviour and then delivers the relevant ads according to each user's interest, which leads to higher targeting accuracy and thus more improved advertising performance. The current user profiling methods include building keywords and topic tags or mapping users onto a hierarchical taxonomy. However, to our knowledge, there is no previous work that explicitly investigates the user online visits similarity and incorporates such similarity into their ad response prediction. In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response. Technically, we propose a transfer learning model based on the probabilistic latent factor graphic models, where the users' ad response profiles are generated from their online browsing profiles. The large-scale experiments based on real-world data demonstrate significant improvement of our solution over some strong baselines.