A UCB-Like Strategy of Collaborative Filtering

By Atsuyoshi Nakamura. 2014

18 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xing Xie
2. Yu Zheng
3. Fuzheng Zhang
4. Guanjie Zheng
5. Nicholas Jing Yuan
6. Zihan Zheng
7. Yang Xiang
8. Zhenhui Jessie Li

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, background): “Recently, some people try to combine bandit with clustering based collaborative filtering [14], andmatrix factorization [6, 21, 32, 42, 43, 51], in order to model more complex user and item relationship, and utilize the social network relationship in determining the reward function.”
• Learning Hidden Features for Contextual Bandits (2016, methodology): “Nakamura developed a UCB-like strategy to perform online collaborative filtering [19].”
• Factorization Bandits for Interactive Recommendation (2017, methodology): “Some other ad-hoc exploration strategies have been proposed in literature (Zhao, Zhang, and Wang 2013; Nakamura 2014), but little is known about their regret bound, or analysis is only provided for overly simplified situations (e.g., the user-item matrix is one rank one and all the latent factors…”