论文阅读7-----基于强化学习的推荐系统 DRN: A Deep Reinforcement Learning Framework for News Recommendation

第7章 采用强化学习技术构建新闻推荐系统：DRN框架及其应用

ABSTRACT

This paper introduces an innovative deep learning-based reinforcement learning approach for news recommendation.

我们提出来RL方法用于新闻推荐。

Online personalized news recommendation presents a significant challenge due to the dynamic nature of both news features and user preferences. Despite the development of several online recommendation models aimed at addressing the dynamic aspect of news recommendations, these approaches still face notable limitations.

新闻推荐面临的重大挑战不仅在于新闻特征和用户偏好呈现出动态变化的趋势存在而且这种趋势的变化速度也难以预测。现有的推荐系统方法在实际应用中存在诸多局限性主要表现在以下几个方面：

First, they only try to model current reward(e.g., Click Through Rate).

1.仅仅尝试当前的奖励，下文引出RL方法，因为RL方法适用于长期的奖励。

Second, limited studies currently consider utilizing user feedback, excluding click/no-click labeling (e.g., how frequent users return), in efforts to aid in enhancing recommendations.

2. 该系统未能充分考虑到用户的反馈意见。即便引入了点击确认（click/no click）标签进行识别,其提供的反馈信息仍然相对贫乏。文中将提及回归时间作为补充措施。

Third, these methods often tend to recommend redundant news to users, which might lead them to feel bored.

3.推荐的物品趋于相似，用户非常不爽。（我们有探索机制，厉害的很）

Therefore, to address the aforementioned challenges,

所以我们的解决方法如下

we introduce a novel recommendation framework built upon deep Q-learning, which is capable of modeling future rewards clearly.

RL的方法，考虑不仅仅只是近期奖励，还有很多未来的奖励。

We additionally consider the user return pattern to serve as an additional supplement to the click/no-click labeling, with the aim of gathering more user feedback data.

将用户离开APP（亦或网页）至再次返回的时间间隔视为反馈机制的一部分，并关注其对用户体验的影响程度

Additionally, an efficient news-seeking approach is integrated to identify new intriguing news for users.

我们的探索机制很厉。

大量系统性实验在离线数据集和在线生产环境中被系统性地执行。

The news recommendation platform has demonstrated superior performance in our methods.

对了，我们还做了实验，的的确确我们超级厉害。

proposed model

用户的表示，state的形成

用户的反馈---本文用用户再次返回时间做了一个feedback

年轻学子们好呀！如果你们对推荐系统科研感兴趣却又感到困惑于如何着手撰写代码的话，请访问我的GitHub存储库或参考由中国人民大学开发的RecBole项目获取相关资料。

https://github.com/xingkongxiaxia/Sequential_Recommendation_System 利用PyTorch实现的当前主流推荐算法

https://github.com/xingkongxiaxia/tensorflow_recommend_system 我在GitHub上找到了一个名为《TensorFlow推荐系统》的项目，并且目前拥有基于深度学习框架的代码实现。

https://github.com/RUCAIBox/RecBole RecBole(各种类型的，超过60种推荐算法）

欢迎大家点小星星