Date: 30 September 2011 (Friday)
5A meeting room, 5th floor, SA block,SE202 Setapak Campus, FES, UTAR, Kuala Lumpur. [map]
Speaker: Cheng Kam Ching
Title: Development of Job Matching Algorithm with Collective Learning Methods
Hiring or job match placing the wrong job seekers to the particular job posted by the employers is a costly mistake. As it is the key to keep the job site remain competitive. The classical online recruiting applications use only simple Boolean operations comparing the basic requirement information to generate the job matched results to job seekers. Therefore, it's often claimed to be irrelevant job matched or too many "hits" . Job seekers must browse through a long list of job advertisements in a given query . The problem of the classical method is about the data modelling. Job seekers need to fill up massive form-based basic requirement information. The data given is too subjective to define its importance. Hence, the matching may not be good. Besides, online recruiting application must be able to discover interesting jobs for recommendation. Job seekers are more likely excited to see a range of different job types in which really match his or her working profiles and own interests. This is the area addressed by this research described in this thesis. An investigation and development of a different method of job matching algorithm was undertaken. The primary objective of this research is to offer an intelligence job matching mechanism incorporated the relevant feedback approach. Real data (job title, job descriptions and job requirements of 3000 job advertisements) from Jobstreet will be pursued with detailed analysis for the validation of the proposed method.