Convolutional Neural Bandit for Visual-aware Recommendation

TL;DR

This paper introduces a CNN-based contextual bandit algorithm for visual-aware recommendation, achieving near-optimal regret bounds and outperforming existing methods on real-world image datasets.

Contribution

It proposes a novel CNN-embedded bandit algorithm with theoretical regret guarantees and demonstrates superior empirical performance in image-based recommendation tasks.

Findings

Achieves near-optimal regret bound of ((((T)))

Outperforms state-of-the-art UCB algorithms on real-world image datasets

Establishes connections with convolutional neural tangent kernel (CNTK)

Abstract

Online recommendation/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 existing in the recommendation procedure. Inspired by the visual-aware recommendation, 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.