Recommender systems (RS) are widely deployed in domains such as e-commerce and online media, yet often intensify the filter bubble problem by repeatedly exposing similar items. While prior studies generally regard such homogeneity as detrimental, its nuanced effects in interactive recommendation systems (IRS) remain underexplored. In particular, the dynamic feedback loop between user preferences and system responses complicates the relationship between homogeneity, diversity, and engagement. This paper presents the first fine-grained analysis of homogeneity in IRS, revealing that homogeneous recommendations can be beneficial in the early repetition stages before becoming detrimental. Building on this insight, we propose Diversity- and Debias-aware Interactive Recommendation (DDIR), an offline reinforcement learning framework that dynamically balances homogeneity and diversity over user sessions. DDIR employs a Transformer-based state encoder to capture time-aware diversity preferences, a homogeneity-friendly diversity model to estimate evolving tolerance to repetition, and a debiased interest model that disentangles intrinsic preferences from conformity bias while considering item quality. These components jointly guide a policy learner to sustain long-term engagement. Experiments on two realistic RL environments, KuaiEnv and KuaiRand, constructed from large-scale short-video logs, demonstrate that DDIR effectively mitigates filter bubbles, increasing average session length by 4.35% and long-term engagement by 25.65% over state-of-the-art baselines in KuaiRand simulated environment. Codes are available at https://github.com/16061025/DDIR.
Li et al. (Tue,) studied this question.