From Exploitation to Balance: Efficient Retargeting Recommendation via DPO-Guided Slot Allocation

Abstract

In e-commerce recommender systems, items that users have previously interacted with are frequently re-recommended, a practice known as retargeting recommendation. While retargeted items offer significantly higher conversion efficiency by leveraging verified user interests, their inherent predictability induces a systemic bias in conventional recommendation pipelines. Specifically, retargeted items are prone to redundant retrieval across multiple recall channels and systematic score inflation by ranking models that over-rely on historical behavior signals. This unchecked dominance results in recommendation homogenization and user fatigue. We term this phenomenon the retargeting homogenization trap, which ultimately erodes long-term platform value. To address this problem, we propose RetargetRec, a novel industrial framework that formulates retargeting recommendation as an independent optimization problem. RetargetRec introduces a retargeting-aware recall module to implement explicit scale control and a Direct Preference Optimization guided by Item-level Reward (DPOIR) paradigm at the ranking stage. By training on item-level preference pairs derived from a composite reward, DPOIR enables optimization beyond immediate feedback signals without the computational overhead of conventional reinforcement learning. Additionally, a preference-based slot allocation mechanism then jointly governs the number, positions, and ordering of retargeted items. Extensive online A/B tests on the Lazada platform demonstrate that RetargetRec effectively suppresses overexposure while delivering significant gains in both transactions (+2.3%) and GMV (+4.4%).