  Estimating position bias is a well-known challenge in Learning to Rank (L2R).
Click data in e-commerce applications, such as targeted advertisements and
search engines, provides implicit but abundant feedback to improve personalized
rankings. However, click data inherently includes various biases like position
bias. Based on the position-based click model, Result Randomization and
Regression Expectation-Maximization algorithm (REM) have been proposed to
estimate position bias, but they require various paired observations of (item,
position). In real-world scenarios of advertising, marketers frequently display
advertisements in a fixed pre-determined order, which creates difficulties in
estimation due to the limited availability of various pairs in the training
data, resulting in a sparse dataset. We propose a variant of the REM that
utilizes item embeddings to alleviate the sparsity of (item, position). Using a
public dataset and internal carousel advertisement click dataset, we
empirically show that item embedding with Latent Semantic Indexing (LSI) and
Variational Auto-Encoder (VAE) improves the accuracy of position bias
estimation and the estimated position bias enhances Learning to Rank
performance. We also show that LSI is more effective as an embedding creation
method for position bias estimation.
