The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

中文翻译：

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社交网络中信息扩散的动态建模，分析和控制：基于深度学习的社交网络推荐算法

该推荐算法可以突破社交网络拓扑结构的限制，增强社交网络上信息（正或负）的交流能力，并在一定程度上指导新闻在社交网络中的信息传递方式。为了解决社交网络新闻推荐中数据稀疏的问题，提出了一种基于深度学习的社交网络推荐算法（DLRASN）。首先，当同一社交网络中的用户浏览信息时，该算法用于以可序列化的方式处理行为数据。然后，引入全局变量来优化Skip-gram中心序列的编码方式，从而了解在线用户的浏览行为习惯。最后，可以通过相似度公式计算出目标用户感兴趣的信息，并推荐给社交网络。实验结果表明，该算法可以提高推荐精度。