  Ranking items regarding individual user interests is a core technique of
multiple downstream tasks such as recommender systems. Learning such a
personalized ranker typically relies on the implicit feedback from users' past
click-through behaviors. However, collected feedback is biased toward
previously highly-ranked items and directly learning from it would result in a
"rich-get-richer" phenomenon. In this paper, we propose a simple yet sufficient
unbiased learning-to-rank paradigm named InfoRank that aims to simultaneously
address both position and popularity biases. We begin by consolidating the
impacts of those biases into a single observation factor, thereby providing a
unified approach to addressing bias-related issues. Subsequently, we minimize
the mutual information between the observation estimation and the relevance
estimation conditioned on the input features. By doing so, our relevance
estimation can be proved to be free of bias. To implement InfoRank, we first
incorporate an attention mechanism to capture latent correlations within
user-item features, thereby generating estimations of observation and
relevance. We then introduce a regularization term, grounded in conditional
mutual information, to promote conditional independence between relevance
estimation and observation estimation. Experimental evaluations conducted
across three extensive recommendation and search datasets reveal that InfoRank
learns more precise and unbiased ranking strategies.
