论文标题
上下文的上下文不确定性，并应用于推荐系统的应用
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
论文作者
论文摘要
经过重复的神经网络已被证明有效地为推荐系统的顺序用户反馈建模。但是，它们通常仅关注项目相关性，并且无法有效地探索用户的各种项目，因此从长远来看会损害系统性能。为了解决这个问题，我们提出了一种新型的复发性神经网络，称为经常性勘探网络（REN），以共同执行代表性学习和潜在空间中的有效探索。 Ren在考虑表示的不确定性的同时，试图平衡相关性和探索。我们的理论分析表明，即使在学识渊博的表示中存在不确定性，REN也可以保留最佳的sublritear遗憾。我们的实证研究表明，REN可以在合成和现实世界的建议数据集上获得令人满意的长期奖励，从而超过最先进的模型。
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.
