互联网时代是一个信息爆炸的时代。到2022年,全球互联网用户已超过40亿,社交媒体用户已经超过30亿。人们每天面对大量的新闻内容,几乎不可能通过浏览所有新闻内容来获得有趣的信息。在这样的背景下,个性化新闻推荐技术得到了广泛的应用,但仍需进一步优化和完善。为了更好地将感兴趣的新闻内容推送给不同的读者,用户对主要新闻网站的满意度应进一步提高。本研究提出了一种基于深度学习和强化学习的推荐算法。首先,提出了基于深度学习的RL算法。深度学习在处理大规模数据和复杂模式识别方面非常出色,但是当涉及到复杂的决策和顺序任务时,它通常面临着低样本效率的挑战。而强化学习(RL)强调通过与环境的交互式学习,通过持续的试验和错误来学习优化策略。与深度学习相比,RL更适合需要长期决策和试错学习的场景。通过反馈行动的奖励信号,能更好地适应未知环境和复杂任务,弥补了深度学习在这些方面的相对不足。将场景应用于动作,以解决新闻传播过程中的顺序决策问题。为了使新闻推荐系统能够考虑用户对新闻内容兴趣的动态变化,深度确定性策略梯度算法应用于新闻推荐场景。相反的学习是对DeepQ网络与战略网络的补充和结合。在充分总结和思考的基础上,本文提出了智能新闻传播推送模式。提出了基于边缘计算技术的新闻传播信息推送流程。最后,基于曲线下面积,提出了RL模型的Q倾斜曲线下面积。该指标可以有效地衡量RL模型的优缺点,并有助于比较模型和评估离线实验。结果表明,DDPG算法与常规推荐算法相比,点击率提高了2.586%。结果表明,本文设计的算法在用户精准推荐方面具有较为明显的优势。本文通过优化智能新闻传播推送模式,有效提高新闻传播效率。此外,本文还深入研究了智能边缘技术在新闻传播中的创新应用,为推动新闻传播方式的发展带来了新的思路和做法。优化智能新闻传播的推送模式,不仅提高了用户体验,同时也为智能边缘技术在该领域的应用提供了强有力的支持,具有重要的实际应用前景。
The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users\' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users\' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects.