TY - GEN
T1 - Inter-departmental research collaboration recommender system based on content filtering in a cold start problem
AU - Purwitasari, Diana
AU - Fatichah, Chastine
AU - Purnama, I. Ketut Eddy
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Indisposition behavior of lecturers to work across university departments is still common in some developing countries. That condition makes little information is known about their preferences of research collaboration. It creates inter-departmental recommendation process similar to a cold-start problem where there is no ground-truth dataset for validating the recommended topics. We propose a recommender system model using data without ground-truth called as uncomprehensive data to help lecturers in their decision making for doing prospective research collaboration. Beside typical recommender system's processes of identifying topic competencies and generating cross-domain topics, our model also includes the process of validating recommended topics without initial ground-truth. We argue that identifying topic process pertain to keyword representation. Therefore, we observed four approaches of topic keyword representation: graph based, matrix based as well as its projected form with latent semantic indexing, and word embedding based which applies a neural network learning. Our results present empirical evidence of cold-start recommendation in a case study of Indonesian state university, which can be guidance for universities with the same circumscribed condition to support their inter-departmental research policies.
AB - Indisposition behavior of lecturers to work across university departments is still common in some developing countries. That condition makes little information is known about their preferences of research collaboration. It creates inter-departmental recommendation process similar to a cold-start problem where there is no ground-truth dataset for validating the recommended topics. We propose a recommender system model using data without ground-truth called as uncomprehensive data to help lecturers in their decision making for doing prospective research collaboration. Beside typical recommender system's processes of identifying topic competencies and generating cross-domain topics, our model also includes the process of validating recommended topics without initial ground-truth. We argue that identifying topic process pertain to keyword representation. Therefore, we observed four approaches of topic keyword representation: graph based, matrix based as well as its projected form with latent semantic indexing, and word embedding based which applies a neural network learning. Our results present empirical evidence of cold-start recommendation in a case study of Indonesian state university, which can be guidance for universities with the same circumscribed condition to support their inter-departmental research policies.
KW - cold start problem
KW - cross-domain collaborative recommendation
KW - latent semantic indexing
KW - word vector
UR - http://www.scopus.com/inward/record.url?scp=85047193558&partnerID=8YFLogxK
U2 - 10.1109/IWCIA.2017.8203581
DO - 10.1109/IWCIA.2017.8203581
M3 - Conference contribution
AN - SCOPUS:85047193558
T3 - 2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings
SP - 177
EP - 184
BT - 2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2017
Y2 - 11 November 2017 through 12 November 2017
ER -