Search

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。

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media 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.)