TY - JOUR
T1 - Swarm collaborative filtering through fish school search
AU - Rozie, Andri Fachrur
AU - In, Hoh Peter
PY - 2014
Y1 - 2014
N2 - In this paper we present an adaptive collaborative filtering algorithm using Fish School Search[1]. The proposed algorithm use not only rating information but also user demographic information and interests to improve similarity measurement. This algorithm adaptive to different user, where it could learn the best combination of features weight, leading to a better prediction. The experiment result shows that the proposed algorithm outperforms other collaborative filtering method. And on our knowledge, this is the first time Fish School Search applied in recommendation system domain.
AB - In this paper we present an adaptive collaborative filtering algorithm using Fish School Search[1]. The proposed algorithm use not only rating information but also user demographic information and interests to improve similarity measurement. This algorithm adaptive to different user, where it could learn the best combination of features weight, leading to a better prediction. The experiment result shows that the proposed algorithm outperforms other collaborative filtering method. And on our knowledge, this is the first time Fish School Search applied in recommendation system domain.
KW - Collaborative filtering
KW - Fish school search
KW - Recommendation systems
UR - http://www.scopus.com/inward/record.url?scp=84897370867&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897370867&partnerID=8YFLogxK
U2 - 10.14257/ijseia.2014.8.3.23
DO - 10.14257/ijseia.2014.8.3.23
M3 - Article
AN - SCOPUS:84897370867
SN - 1738-9984
VL - 8
SP - 251
EP - 254
JO - International Journal of Software Engineering and its Applications
JF - International Journal of Software Engineering and its Applications
IS - 3
ER -