Treatment Effect Estimation for User Interest Exploration on Recommender Systems

TL;DR

This paper introduces UpliftRec, a novel framework that models top-N recommendation as a treatment effect estimation problem, effectively uncovering hidden user interests and optimizing overall click-through rates in recommender systems.

Contribution

UpliftRec is the first to treat recommendation as a treatment effect estimation problem, integrating uplift modeling with dynamic programming for global category scheduling.

Findings

UpliftRec outperforms baseline methods in discovering hidden interests.

It achieves higher recommendation accuracy across three datasets.

The framework effectively balances exploration and exploitation.

Abstract

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.