Improving position bias estimation against sparse and skewed dataset with item embedding

Estimating position bias is a well-known challenge in Learning to rank (L2R). Click data in e-commerce applications, such as advertisement targeting and search engines, provides implicit but abundant feedback to improve personalized rankings. However, click data inherently include various biases like position bias. Click modeling is aimed at denoising biases in click data and extracting reliable signals. Result Randomization and Regression Expectation-maximization algorithm have been proposed to solve position bias. Both methods require various pairs of observations (item, position). However, in real cases of advertising, marketers frequently display advertisements in a fixed pre-determined order, and estimation suffers from it. We propose this sparsity of (item, position) in position bias estimation as a novel problem, and we propose a variant of the Regression EM algorithm which utilizes item embeddings to alleviate the issue of the sparsity. With a synthetic dataset, we first evaluate how the position bias estimation suffers from the sparsity and skewness of the logging dataset. Next, with a real-world dataset, we empirically show that item embedding with Latent Semantic Indexing (LSI) and Variational autoencoder (VAE) improves the estimation of position bias. Our result shows that the Regression EM algorithm with VAE improves RMSE relatively by 10.3% and EM with LSI improves RMSE relatively by 33.4%.