Context Uncertainty in Contextual Bandits with Applications to Recommender Systems

• URL: http://arxiv.org/abs/2202.00805v1
• Date: Tue, 1 Feb 2022 23:23:50 GMT
• Title: Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
• Authors: Hao Wang, Yifei Ma, Hao Ding, Yuyang Wang
• Abstract summary: 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. Our theoretical analysis shows that REN can preserve the rate-linear suboptimal 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.
• Score: 16.597836265345634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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