Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. REN tries to balance relevance and exploration while taking into account the uncertainty in the representations. Our theoretical analysis shows that REN can preserve the rate-optimal sublinear regret even when there exists uncertainty in the learned representations. Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, outperforming state-of-the-art models.

该论文提出了一种新型的循环探索网络，用于在潜在空间中进行表示学习和有效的探索，以平衡相关性和多样性，同时考虑表示中的不确定性，理论分析表明，该网络即使存在学习表示中的不确定性，也能保持速率最优的次线性遗憾，实证研究证明了该网络在综合和真实推荐数据集上能够实现令人满意的长期奖励，优于现有最先进的模型。

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