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Convolutional Neural Bandit for Visual-aware Recommendation

arXiv 2107.07438 ↗First posted 2 Jul 2021cs.LG cs.CVcited by 0

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Abstract (arXiv)

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 tildeO(√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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 2 Jul 2021.

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References · 16

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  3. 2018A Convergence Theory for Deep Learning via Over-Parameterizationcited by 784
  4. 2018Gradient Descent Finds Global Minima of Deep Neural Networkscited by 572
  5. 2019On Exact Computation with an Infinitely Wide Neural Netcited by 572
  6. 2019Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networkscited by 221
  7. 2013Finite-Time Analysis of Kernelised Contextual Banditscited by 177
  8. 2018Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Samplingcited by 166
  9. 2019Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivationcited by 146
  10. 2020Neural Thompson Samplingcited by 127
  11. 2014Online Clustering of Banditscited by 99
  12. 2017Multi-Task Learning for Contextual Banditscited by 47
4 more
  1. 2021Local Clustering in Contextual Multi-Armed Banditscited by 29
  2. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  3. 2020Generic Outlier Detection in Multi-Armed Banditcited by 14
  4. 2021A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertisingcited by 14

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