关键词: 微博搜索, 深度卷积神经网络, 深度学习, 搜索排序, 信息搜索

Abstract: Focusing on the problem that traditional information search method can not realize contents search for specific topics directly, this paper proposes a microblog search method based on convolutional neural networks to search security topics related content from microblogs with matching and ranking. The proposed method is composed of related microblog content filter and microblog content matching, both of which are based on deep convolutional neural networks. The content filter aims at selecting security topic related microblog content according to convolutional feature representations. The content matching outputs the sequence of results which are ranked by similarities through the processes of non-linear feature transformation. The content filter and the content matching operate the non-linear feature transformation of mined local semantic features based on convolution and max-pooling calculations to achieve similarity matching. Experimental results demonstrate that the proposed method performs better compared with those of the state-of-the-art methods， and also verify the effectiveness of the proposed method on microblog content search on specific topics.

Key words: microblog search, deep convolutional neural networks, deep learning, search ranking, information search