  Recommender systems learn personalized user preferences from user feedback
like clicks. However, user feedback is usually biased towards partially
observed interests, leaving many users' hidden interests unexplored. Existing
approaches typically mitigate the bias, increase recommendation diversity, or
use bandit algorithms to balance exploration-exploitation trade-offs.
Nevertheless, they fail to consider the potential rewards of recommending
different categories of items and lack the global scheduling of allocating
top-N recommendations to categories, leading to suboptimal exploration. In this
work, we propose an Uplift model-based Recommender (UpliftRec) framework, which
regards top-N recommendation as a treatment optimization problem. UpliftRec
estimates the treatment effects, i.e., the click-through rate (CTR) under
different category exposure ratios, by using observational user feedback.
UpliftRec calculates group-level treatment effects to discover users' hidden
interests with high CTR rewards and leverages inverse propensity weighting to
alleviate confounder bias. Thereafter, UpliftRec adopts a dynamic programming
method to calculate the optimal treatment for overall CTR maximization. We
implement UpliftRec on different backend models and conduct extensive
experiments on three datasets. The empirical results validate the effectiveness
of UpliftRec in discovering users' hidden interests while achieving superior
recommendation accuracy.
