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论文阅读14-----强化学习在推荐系统中的应用

深度学习强化学习推荐系统

There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging.

2021-01-22

9681

论文阅读13-----基于强化学习的推荐系统

推荐系统深度学习强化学习

Applying reinforcement learning (RL) in recommender systems is attractive but costly due to the constraint of the interaction with

2021-01-21

9800

论文阅读11-----基于强化学习的推荐系统

深度学习推荐系统强化学习

Abstract Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for longrun performance.

2021-01-19

6870

论文阅读10-----基于强化学习的互联网应用

推荐系统强化学习深度学习

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL fo

2021-01-19

4790

论文阅读9-----基于强化学习的推荐系统

推荐系统强化学习深度学习

With the recent advances in Reinforcement Learning (RL),there have been tremendous interests in employing RL fo recommender systems. However, directly training and evaluating a new RL-based recommendation algorithm needs to

2021-01-18

7050

论文阅读8-----基于强化学习的推荐系统

深度学习推荐系统强化学习

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.

2021-01-18

1.1K1

论文阅读7-----基于强化学习的推荐系统

推荐系统深度学习强化学习

In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation.

2021-01-18

6030

论文阅读6-----基于强化学习的推荐系统

深度学习推荐系统强化学习

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users’ personalized items or services.

2021-01-18

5601

论文阅读5-----基于强化学习的推荐系统

深度学习强化学习推荐系统

Recommender systems can mitigate the information overload problem by suggesting users’ personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is – users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems

2021-01-17

5200

论文阅读4-----基于强化学习的推荐系统

推荐系统强化学习深度学习

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy.

2021-01-16

7140

论文阅读3-----基于强化学习的推荐系统

深度学习强化学习推荐系统

problems in recommendation: a complex user state space (但好在有很多隐式的数据可以使用）

2021-01-15

1.1K0

论文阅读2-----基于强化学习的推荐系统

推荐系统强化学习 linux

特别多的状态和动作空间会造成较低的credit assignment problem and low quality reward signal.

2021-01-14

1K1

论文阅读-----强化学习在推荐系统中的应用

推荐系统强化学习深度学习

看这篇文章主要是在知乎和腾讯云上看的，主要是文章发在KDD2019上没有下载渠道。这篇文章主要的亮点在于对feedback，dwellingtime，return backtime等的考虑来提高用用户的长期喜爱度。

2021-01-14

9590

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