Robust transfer integrated locally kernel embedding for click-through rate prediction

Abstract

With the rapid development of online advertising, the click-through rate (CTR) prediction plays an important role in improving the benefits of advertising and user experience. CTR is the most commonly used evaluation indicator of the effects of online advertising. At present, the keys including feature extraction and user click behavior modeling have been taken into consideration by many researchers to design methods for CTR prediction. However, the characteristics of high-dimensional data sparseness and imbalance in advertising data are not fully considered, which results in the insufficient utilization of advertising information. To alleviate the problems of data sparsity and imbalance, this paper proposes a robust integrated locally kernel embedding (RILKE) model to solve data sparseness and incorporate unsupervised transfer learning into RILKE to form an improved model, named robust transfer integrated locally kernel embedding (RTILKE). Through theoretical analysis and empirical experiments, RTILKE can efficiently solve the data-imbalance problem of CTR prediction in online advertising, which effectively improves the prediction performance of advertising responses.