TY - JOUR
T1 - Hybrid occupation recommendation for adolescents on interest, profile, and behavior
AU - Ochirbat, Ankhtuya
AU - Shih, Timothy K.
AU - Chootong, Chalothon
AU - Sommool, Worapot
AU - Gunarathne, W. K.T.M.
AU - Wang, Hai Hui
AU - Ma, Zhao Heng
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/6
Y1 - 2018/6
N2 - Young people in high school or college make critical decisions regarding what major to study and which career path to pursue. But, many students enter post-secondary education without a clear idea of their major and future career plans. Discovering students’ suitable occupations as early as possible can help them to choose an appropriate vocational learning direction and to build the skills and the abilities for the prospective occupation. For those reasons, students need an automatic counseling system. In order to do this, recommendation methods were employed; it aims to counsel suitable occupation for students, to discover their occupational interests and to guide them to improve their skills. We implemented a hybrid recommendation system called occupation recommendation (OCCREC) that integrates content-based and collaborative filtering methods. We involved three sets of information including student's profiles, vocational interests, and their behaviors. The student profile contains two types of data, namely, background and interest/hobby retrieved from Facebook. In the experiment, the students from four countries consisted of Mongolia, Sri Lanka, Taiwan, and Thailand used the OCCREC. And, five occupations were shown to the students by using five similarity measures which are Euclidean, Intersection, Cosine, Jaccard, and Pearson. Finally, OCCREC allows students to rate the results accordingly based on user's satisfied scores and to share their experiences on Facebook.
AB - Young people in high school or college make critical decisions regarding what major to study and which career path to pursue. But, many students enter post-secondary education without a clear idea of their major and future career plans. Discovering students’ suitable occupations as early as possible can help them to choose an appropriate vocational learning direction and to build the skills and the abilities for the prospective occupation. For those reasons, students need an automatic counseling system. In order to do this, recommendation methods were employed; it aims to counsel suitable occupation for students, to discover their occupational interests and to guide them to improve their skills. We implemented a hybrid recommendation system called occupation recommendation (OCCREC) that integrates content-based and collaborative filtering methods. We involved three sets of information including student's profiles, vocational interests, and their behaviors. The student profile contains two types of data, namely, background and interest/hobby retrieved from Facebook. In the experiment, the students from four countries consisted of Mongolia, Sri Lanka, Taiwan, and Thailand used the OCCREC. And, five occupations were shown to the students by using five similarity measures which are Euclidean, Intersection, Cosine, Jaccard, and Pearson. Finally, OCCREC allows students to rate the results accordingly based on user's satisfied scores and to share their experiences on Facebook.
KW - Collaborative filtering
KW - Content-based method
KW - Holland model
KW - Hybrid recommendation
KW - Occupation recommendation
KW - Vocational Interests
UR - http://www.scopus.com/inward/record.url?scp=85013665947&partnerID=8YFLogxK
U2 - 10.1016/j.tele.2017.02.002
DO - 10.1016/j.tele.2017.02.002
M3 - 期刊論文
AN - SCOPUS:85013665947
SN - 0736-5853
VL - 35
SP - 534
EP - 550
JO - Telematics and Informatics
JF - Telematics and Informatics
IS - 3
ER -