| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
28 March 2024 |
|
| | | |
|
Article overview
| |
|
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems | Hao Wang
; Yifei Ma
; Hao Ding
; Yuyang Wang
; | Date: |
2 Feb 2022 | Abstract: | Recurrent neural networks have proven effective in modeling sequential user
feedbacks for recommender systems. However, they usually focus solely on item
relevance and fail to effectively explore diverse items for users, therefore
harming the system performance in the long run. To address this problem, we
propose a new type of recurrent neural networks, dubbed recurrent exploration
networks (REN), to jointly perform representation learning and effective
exploration in the latent space. REN tries to balance relevance and exploration
while taking into account the uncertainty in the representations. Our
theoretical analysis shows that REN can preserve the rate-optimal sublinear
regret even when there exists uncertainty in the learned representations. Our
empirical study demonstrates that REN can achieve satisfactory long-term
rewards on both synthetic and real-world recommendation datasets, outperforming
state-of-the-art models. | Source: | arXiv, 2202.00805 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |