TY - GEN
T1 - Collaborative filtering based on the latent class model for attributes
AU - Kobayashi, Manabu
AU - Mikawa, Kenta
AU - Goto, Masayuki
AU - Matsushima, Toshiyasu
AU - Hirasawa, Shigeichi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation.
AB - In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior of customers and services with various attributes for marketing. We assume that each customer and service have the invisible attribute which is called latent class. Assuming a combination of attribute values of a customer and service is classified to a latent class, furthermore, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class combination of a customer, service and attribute values. Then, we show the method to estimate parameters of a statistical model based on the variational Bayes method and the mean field approximation. Consequently, we show the effectiveness of the proposed model and the estimation method by simulation.
KW - collaborative filtering
KW - electric commerce
KW - latent class model
KW - mean field approximation
KW - variational Bayes method
UR - http://www.scopus.com/inward/record.url?scp=85048508116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048508116&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2017.00-42
DO - 10.1109/ICMLA.2017.00-42
M3 - Conference contribution
AN - SCOPUS:85048508116
T3 - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
SP - 893
EP - 896
BT - Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
A2 - Chen, Xuewen
A2 - Luo, Bo
A2 - Luo, Feng
A2 - Palade, Vasile
A2 - Wani, M. Arif
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Y2 - 18 December 2017 through 21 December 2017
ER -