Integrating Case-Based Reasoning in Job Matching System for Pre-selection Process of Recruitment

The progress of Internet and World Wide Web technology brings the movement of traditional recruitment process to web based recruitment. Applying job matching approach automatically will bring benefit to both job seekers and employers. For the employer, the costs of manually preselecting potential candidates have risen and employers are searching for means to automate the preselecting of candidates. A few techniques could be applied in order to implement job matching process such as using fuzzy matching, semantic, rule-base reasoning and case–based reasoning (CBR). This study aims to demonstrate that CBR could be integrated in job matching to recommend the best candidate suitable with the job requirement using similarity measurement. As a result, a prototype called Intelligent Agent Dot Com (IADC) using CBR engine for matching purposes has been developed, validated and evaluated in this study. The finding through validation and evaluation phase indicates that IADC is reliable to assist employer in the pre-selection process during recruitment. In fact, the pre-selection of candidates has become easier than the manual process

Evaluating student-internship fit by using fuzzy linguistic terms and a fuzzy OWA operator

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksPersonnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate’s interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a student’s interests is selected using this approach. The mplementation of the proposed method is illustrated using a numerical example in a business application.Postprint (author's final draft

An Intelligent Based Screening Agent for Job Recruitment

Presently, companies and organizations face a lot of stress and complications to acquire intelligent and qualified employees. A lot of expert system has been developed to elucidate the process but they are not intelligently based enough. In this paper, an intelligent based screening agent is proposed. The advertised positions and necessary requirements are posted by company or organization. The applicant logon the system and apply for an advertised position, filling necessary form to generate his/her attribute and submit. Knowledge base for the applicants and the requirements of the company or organizations are created using Microsoft Access and an inference engine then developed to assign an applicant to a proper job requirement and finally create qualified list and unqualified list for assigned and unassigned cases.  The system was implemented using Visual Basic 6.0. Keywords: Intelligent, Screening, Recruitment, Human Resource (HR) Personnel, Expert System, Knowledge Base

Pencarian Lowongan Pekerjaan Berbasis Agen Berdasarkan Profil Pencari Kerja dengan Pendekatan Semantic Web Service

Currently, job searching service still has many weaknesses and often fails to provide relevant job information that matches the needs of job seekers. This is due to the searching method applied in the search engines still uses the syntax-based matching and the lack of integration among the job service providers. Therefore it’s difficult for the job seekers to get the desired information. To overcome these weaknesses, a prototype of a job vacancy searching by involving a web service as a job information provider is proposed.This thesis is aimed to create job search based on the personalization of job seeker by combining multi agent  and semantic web service approaches.The designing of the prototype used a multiagent technology whose capability was to call job service provider and run matching process of the job vacancy appropriate with the job seeker’s profile automatically. Algorithm of the service selection used service matching and Simple Additive Weighting. The similary between the job offer and the job seeker’s profile was calculated by using semantic algorithm. Based on the testing carried out to the respondents, it’s stated that this prototype has been able to give recommendation of job appropriate with the job seeker’s

Expert recommendation based on social drivers, social network analysis, and semantic data representation

ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration

Accurate and efficient profile matching in knowledge bases

A profile describes a set of properties, e.g. a set of skills a person may have, a set of skills required for a particular job, or a set of abilities a football player may have with respect to a particular team strategy. Profile matching aims to determine how well a given profile fits to a requested profile and vice versa. The approach taken in this article is grounded in a matching theory that uses filters in lattices to represent profiles, and matching values in the interval [0,1]: the higher the matching value the better is the fit. Such lattices can be derived from knowledge bases to represent the knowledge about profiles. An interesting question is, how human expertise concerning the matching can be exploited to obtain most accurate matchings. It will be shown that if a set of filters together with matching values by some human expert is given, then under some mild plausibility assumptions a matching measure can be determined such that the computed matching values preserve the relevant rankings given by the expert. A second question concerns the efficient querying of databases of profile instances. For matching queries that result in a ranked list of profile instances matching a given one it will be shown how corresponding top-k queries can be evaluated on grounds of pre-computed matching values. In addition, it will be shown how the matching queries can be exploited for gap queries that determine how profile instances need to be extended in order to improve in the rankings