Nudging neural click prediction models to pay attention to position

Predicting the click-through rate (CTR) of an item is a fundamental task in online advertising and recommender systems. CTR prediction models are typically trained on user click data from traffic logs. However, users are more likely to interact with items that were shown prominently on a website. CTR models often overestimate the value of such items and show them more often, at the expense of items of higher quality that were previously shown at less prominent positions. This self-reinforcing position bias effect reduces both the immediate and long-term quality of recommendations for users. In this paper, we revisit position bias in a family of state-of-the-art neural models for CTR prediction, and use synthetic data to demonstrate the difficulty of controlling for position. We propose an approach that encourages neural networks to use position (or other confounding variables) as much as possible to explain the training data, and a metric that can directly measure bias. Experiments on two real-world datasets demonstrate the effectiveness of our approach in correcting for position-like features in 2 state-of-the-art CTR prediction models.