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User behaviour targeting is essential in online advertising. Compared withsponsored search keyword targeting and contextual advertising page contenttargeting, user behaviour targeting builds users' interest profiles viatracking their online behaviour and then delivers the relevant ads according toeach user's interest, which leads to higher targeting accuracy and thus moreimproved advertising performance. The current user profiling methods includebuilding keywords and topic tags or mapping users onto a hierarchical taxonomy.However, to our knowledge, there is no previous work that explicitlyinvestigates the user online visits similarity and incorporates such similarityinto their ad response prediction. In this work, we propose a general frameworkwhich learns the user profiles based on their online browsing behaviour, andtransfers the learned knowledge onto prediction of their ad response.Technically, we propose a transfer learning model based on the probabilisticlatent factor graphic models, where the users' ad response profiles aregenerated from their online browsing profiles. The large-scale experimentsbased on real-world data demonstrate significant improvement of our solutionover some strong baselines.

Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation