Treatment Effect Estimation for User Interest Exploration on Recommender Systems
arXiv:2405.08582 · doi:10.1145/3626772.3657736
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.
Accepted to SIGIR 2024
References in corpus (13)
- Causal Intervention for Leveraging Popularity Bias in Recommendation
- Nonparametric methods for doubly robust estimation of continuous treatment effects
- AutoDebias: Learning to Debias for Recommendation
- KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
- Degenerate Feedback Loops in Recommender Systems
- Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
- A Survey on Causal Discovery: Theory and Practice
- Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis
- User-controllable Recommendation Against Filter Bubbles
- Unbiased Learning for the Causal Effect of Recommendation
- Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
- LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
- Graph Neural Bandits