Design And Implementation Of Personalized Recommendation System Based On Mobile Employment Information

With the development of information technology, to job recruitment site for information on behalf of the employment service to become a personal job, corporate recruiters main channel. But with the rapid development of the Internet, the current employment information services can not meet the needs of job seekers, reflected in two aspects:First, the rapid development of mobile Internet, and most of the employment service agencies are not aware of the mobile Internet has brought the tremendous opportunities and challenges; two, third-party employment services agency after another, serious redundancy employment information, job seekers difficult to efficiently obtain useful information on employment, job search inefficient.In this context, the subject of personalized recommendation system based on mobile services and employment information has been made to achieve a strong practical significance, the main work includes1A structural employment information acquisition engine was implemented by using Web crawlers and information extraction. Firstly grab job recruitment site by third-party web crawler way. Then through the information collection, information extraction technology for cleaning and finishing jobs. Finally, de-emphasis and stored in MySql database to form a standard employment information datasets.2Implement a engine which can acquire and process mobile user’s behavior. Job user interaction mobile client and server via the interface, user-generated employment in job behavior (browse, collection application, search, etc.), server monitoring these interfaces and by certain rules of conduct to be converted to the corresponding scoring matrix and stores. 3This paper also implements an engine that provide users Personalized recommendation Employment Information. The use of the user’s job history behavior data, an improved design based on the results of library time decay recommended collaborative filtering recommendation algorithm using offline recommended strategies will produce the results stored in the recommended users.4Upon the achieved goles,this paper also implements a common laborer employment information service platform. Restful style employment information to achieve public service platform to facilitate client-side extension.5A mobile client software was implemented by using Android technology, Achieved the ultimate goal of this paper to provide services personalized and Comprehensive.In this paper, through the study of information technology to achieve the acquisition of multi-source, mass employment information integration; through improved collaborative filtering recommendation algorithm to solve the employment information redundancy, improve job efficiency; Design and Implementation of REST-style API interface, and complete the mobile client software development, providing efficient, personalized employment services to job user information...