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新闻推荐系统对新媒体新闻传播有着重要作用。提出了一种以深度强化学习为基础的推荐系统，旨在结合神经网络的表征能力和强化学习的策略选择能力来提升新闻推荐效果。使用动态动作掩码加强对用户短期兴趣的判断能力，使用优化缓存机制提升经验缓存的使用效率，通过区域遮蔽性质的奖励设计加快模型训练，从而提高推荐系统在新闻推荐领域的表现。实验表明，所提模型在新闻数据集上的推荐准确率与主流的神经网络推荐方法相当，且在排序性能上优于当前先进的推荐算法。

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that the multi-modal fusion effect is not good, and the key emotional information from specific time periods and multiple perspectives cannot be fully mined, this paper proposed a time-series multi-modal emotional classification model based on multiple perspectives to extract key information from multiple perspectives in a specific time period. Firstly, it performed low-dimensional spatial word embedding and sequence expression on the data from the two perspectives of text title and text content, extracted multi-modal time series features from different perspectives, and performed feature extraction on the data from two perspectives of image interception and horizontal mirroring. Secondly, it used the recurrent neural network to construct the time series interaction features of multi-modal data to increase mutual information. Finally, joint training was performed based on contrastive learning to complete sentiment classification. The model was evaluated on two multi-modal sentiment classification benchmark datasets Yelp and Mutli-Zol, with accuracies of 73.92% and 69.15%, respectively. Comprehensive experiments show that multi-view multi-modal sentence sequences in specific time periods can improve model performance. [ABSTRACT FROM AUTHOR]