Convolutional Neural Bandit: Provable Algorithm for Visual-aware Advertising

• URL: http://arxiv.org/abs/2107.07438v1
• Date: Fri, 2 Jul 2021 03:02:29 GMT
• Title: Convolutional Neural Bandit: Provable Algorithm for Visual-aware Advertising
• Authors: Yikun Ban, Jingrui He
• Abstract summary: Contextual multi-armed bandit has shown success in the application of advertising to solve the exploration-exploitation dilemma existed in the recommendation procedure. Inspired by the visual-aware advertising, in this paper, we propose a contextual bandit algorithm.
• Score: 41.30283330958433
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online advertising is ubiquitous in web business. Image displaying is considered as one of the most commonly used formats to interact with customers. Contextual multi-armed bandit has shown success in the application of advertising to solve the exploration-exploitation dilemma existed in the recommendation procedure. Inspired by the visual-aware advertising, in this paper, we propose a contextual bandit algorithm, where the convolutional neural network (CNN) is utilized to learn the reward function along with an upper confidence bound (UCB) for exploration. We also prove a near-optimal regret bound $\tilde{\mathcal{O}}(\sqrt{T})$ when the network is over-parameterized and establish strong connections with convolutional neural tangent kernel (CNTK). Finally, we evaluate the empirical performance of the proposed algorithm and show that it outperforms other state-of-the-art UCB-based bandit algorithms on real-world image data sets.

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