关键词: 信息抽取, 推荐, 协同过滤, 规则, 统计, 简历

Abstract: In order to solve the problem of laborious and time-consuming artificial selection from mass electronic resumes, a solution to resumes automatic extraction and recommendation is proposed. Firstly, the sentences in Chinese resume are represented as vectors through word segmentation, part-of-speech tagging and other preprocessing steps, then SVM classification algorithm is used to classify the sentences into six predefined general classes, such as personal basic information, job intension, working experience and so on. Secondly, according to the lexical and grammatical features of personal basic information block, the rules are constructed by hand to extract the key information like Name, Gender, and Contact information. While the HMM model is used to extract the detailed information in complex information blocks, and puts forward rules and statistics based resume information extraction method. Finally, a Content-Based Reciprocal Recommender algorithm （CBRR） is proposed, which takes into account the preferences of both enterprise and job seekers. The experiment results show that the solution proposed in this paper can assist enterprises in recruitment, improve screening efficiency and save recruitment costs.

Key words: information extraction, recommendation, collaborative filtering, rule, statistics, resume