Data Analysis and Knowledge Discovery in Web Recruitment—Based on Big Data Related Jobs

This paper mines and analyzes the post information of the online recruitment data, and discovers the knowledge in large-scale web data, so as to achieve the precise connection between professional job demand and supply. First, internet crawler technology is adopted to acquire data. Second, the authors digitalize polymorphic data and conduct Chinese word segmentation, stop word filtering and other operations on data records. Third, the cosine similarity is used to measure the similarity of the vector, and the K-means++ is used for post clustering. Then, latent dirichlet allocation and apriori are used for post correlation analysis. Last, the authors use auto-encoder to achieve job matching recommendation.