Abstract: Gender prediction has evoked great research interests due to its potential applications,like targeted advertisement and personalized search.Most of existing studies rely on the content texts.However,the text information is hard to access.This makes it difficult to extract text features.In this paper,we proposed a novel frame-work which only involves the users’ ID for gender prediction.The key idea is to represent users in the embedding connection space.We presented two strategies to modify the word embedding technique for user embedding.The first is to sequentialize users’ ID to get the order of social context.The second is to embed users into a large-sized sliding window of contexts.We conducted extensive experiments on two real data sets from Sina Weibo.Results show that our method is significantly better than the state-of-the-art graph embedding baselines.Its accuracy also outperforms that of the content based approaches.

Key words: Gender prediction,Users in social media,Social contexts,Social representations