Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringTransfer LearningSimilar Papers 제목 키워드 기반
Statistical Scenario Modelling and Lookalike Distributions for Multi-Variate AI Risk
Evaluating AI safety requires statistically rigorous methods and risk metrics for understanding how the use of AI affects aggregated risk. However, much AI safety literature focuses upon risks arising from AI models in i…
BenchmarkingVulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins
Face morphing attacks have emerged as a potential threat, particularly in automatic border control scenarios. Morphing attacks permit more than one individual to use travel documents that can be used to cross borders usi…
BenchmarkingFace RecognitionCan Modern Vision Models Understand the Difference Between an Object and a Look-alike?
Recent advances in computer vision have yielded models with strong performance on recognition benchmarks; however, significant gaps remain in comparison to human perception. One subtle ability is to judge whether an imag…
Cross-Modal RetrievalExploring 360-Degree View of Customers for Lookalike Modeling
Lookalike models are based on the assumption that user similarity plays an important role towards product selling and enhancing the existing advertising campaigns from a very large user base. Challenges associated to the…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display, video, or social. Given a set of audien…
MarketingMeta-LearningRecommendation Systems