The Research On YX Company's Internal Job Recommendation Model

With the arrival of large data age,all walks of life have been playing more and more attention to the various types of basic data in the industry.Amazon's former chief scientist Andreas Weigend said: "data is the new oil!" Today the big data is prevailing,the value of large data analysis has gone beyond the imagination of most people.Therefore,YX company always concerns about the company's human resources data,in today's recruitment background in the market which is "difficult recruitment and difficult employment".Then YX company statistics and analysis those data from various perspectives.According to that,YX company can develop a scientific,timely and effective human resources management system to ensure that every employee can maintain a high enthusiasm for work at all times,and give full play to their talents.So the whole performance of the company's employees will raise substantially.In order to reduce the turnover rate of the employees of the company and ensure the stability of the employees in the market environment where the "recruitment is difficult",YX company has decided to design the job recommendation model with the characteristics of the company to provide the staff who want to transfer or leave with job referral service.As much as possible to reduce the company's staff turnover,thereby reducing staff recruitment costs.In order to achieve the above objectives,this paper carries out the project based on the internal position.Firstly,this paper defines the attribute range of users.And then divides these users into two parts,users with browsing behavior records and users without browsing behavior,according to the situation of the user's basic information and the browsing behavior records.And for these two different user types,respectively,based on case-based reasoning and model-based collaborative filtering recommendation algorithm to provide users with recommended services to ensure the accuracy of the recommended results.This paper not only takes into account the user's intention attribute information about the job,but also takes into account the employee's satisfaction attribute and the freshness attribute of the position,which greatly improves the success rate of the system user's turnover and greatly reduces the employee's loss rate of AI,to ensure the stability of its staff.