Transferable Contextual Bandits with Prior Observations
Transferable Contextual Bandits with Prior Observations
复制标题
具有先前观察的可转移上下文强盗
DOI：
10.1007/978-3-030-75765-6_32
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wu, Xintao
中科院分区：
文献类型：
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作者：
Labille, Kevin;Huang, Wen;Wu, Xintao
在传统的推荐系统中，跨域推荐的研究由来已久，特别是为了解决冷启动问题。虽然最近的动态个性化推荐方法利用了上下文强盗的力量来受益于开发-探索范式，但在这种背景下进行跨域推荐的工作很少。我们提出了一种新的方法，通过跨域的方法来解决上下文盗贼环境下的冷启动问题。我们开发的算法T-LinUCB利用来自多个域的先验推荐观测来初始化新ARM的参数，以避免由于冷启动问题而导致的数据不足。因此，我们的强盗一开始就拥有知识，从而产生更好的推荐和更快的收敛。我们提供了后悔分析和实验评估。我们的方法比基准，LinUCB，和实验结果证明了我们的模型的好处。
Cross-domain recommendations have long been studied in traditional recommender systems, especially to solve the cold-start problem. Although recent approaches to dynamic personalized recommendation have leveraged the power of contextual bandits to benefit from the exploitation-exploration paradigm, very few works have been conducted on cross-domain recommendation in this setting. We propose a novel approach to solve the cold-start problem under the contextual bandit setting through the cross-domain approach. Our developed algorithm, T-LinUCB, takes advantage of prior recommendation observations from multiple domains to initialize the new arms’ parameters so as to circumvent the lack of data arising from the cold-start problem. Our bandits therefore possess knowledge upon starting which yields better recommendation and faster convergence. We provide both a regret analysis and an experimental evaluation. Our approach outperforms the baseline, LinUCB, and experiment results demonstrate the benefits of our model.
使用多臂老虎机解决电信公司推荐系统中的冷启动问题
DOI：
10.1007/978-3-319-13817-6_3
发表时间：
2014
期刊：
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
影响因子：
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作者：
H. Nguyen;Anders Kofod
通讯作者：
Anders Kofod