Word embedding based retrieval model for similar cases recommendation

Similar cases recommendation is more and more popular in the internet inquiry. There have been lots of cases which have been solved perfectly, and recommending them to similar inquiries can not only save the patients' waiting time, but also giving more good references. However, the inquiry platform cannot understand the diversity of description, i.e. the same meaning with different description. This may shield some cases with very high quality answers. In this paper, based on deep learning, we proposed a retrieval model combining word embedding with language models. We use word embedding to solve the problem of description diversity, and then recommend the similar cases for the inquiries. The experiments are based on the data from ask.39.net, and the results show that our methods outperform the state-of-art methods.