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

TL;DR

This paper introduces a transfer learning framework that models user browsing similarity to improve ad response prediction, significantly enhancing targeting accuracy in online advertising.

Contribution

It presents a novel transfer learning approach using probabilistic latent factor models to incorporate user browsing similarity into CTR estimation.

Findings

Significant improvement over baseline models in real-world data

Effective modeling of user browsing similarity for ad response prediction

Demonstrated scalability in large-scale experiments

Abstract

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.