利用 Bandit 算法设计的推荐算法可以较好地解决上述问题。 根据是否考虑上下文特征,Bandit算法分为context-free bandit和contextual bandit两大类。 算法伪代码(single-play bandit algorithm): 与传统方法的区别: 每个候选商品学习一个独立的模型,避免传统大一统模型的样本分布不平衡问题 传统方法采用贪心策略,尽最大可能...
We address the problem of competing with any large set of N policies in the nonstochastic bandit setting, where the learner must repeatedly select among K actions but observes only the reward of the chosen action. We present a modification of the Exp4 algorithm of Auer et al...
L. Li, W. Chu, J. Langford, and X. Wang, “Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms,” in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2011, pp. 297–306. 本文作者:张相於([e...
Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. \emph{Offline} evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user...
Many of the algorithms here oftentimes don't manage to beat simpler benchmarks (e.g. Offset Tree vs. a naïve One-Vs-Rest using only subsets of the data for each classifier), and I wouldn't recommend relying on them. They are nevertheless provided for comparison purposes. ...
L. Li, W. Chu, J. Langford, and X. Wang, “Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms,” in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2011, pp. 297–306. ↩...
L. Li, W. Chu, J. Langford, and X. Wang, “Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms,” in Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, New York, NY, USA, 2011, pp. 297–306. ...
Neural network-based contextual bandit algorithms (Riquelme et al. 2018; Zahavy and Mannor 2019)没有理论保证。我们能设计可证明有效的nn-based算法来学习一般的奖励函数吗? yes! NeuralUCB! I神经网络用于建立奖励函数模型,UCB策略用于探索 I根号T的regret理论保证 IMatches regret bound for linear setting (...
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because their exploration has to be performed in the entire neural network parameter space to obtain nearly optimal regret, the resulting...
Multi-armed banditContext-awareReinforcement learning algorithms play an important role in modern day and have been applied to many domains. For example, personalized recommendations problem can be modelled as a contextual multi-armed bandit problem in reinforcement learning. In this paper, we propose ...