TY - GEN
T1 - Online Recommendation Based on Collaborative Topic Modeling and Item Diversity
AU - Liu, Duen-Ren
AU - Chou, Yun Cheng
AU - Jian, Ciao Ting
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/8
Y1 - 2018/7/8
N2 - Online news websites provide diverse article topics, such as fashion news, entertainment and movie articles to attract more users and create more benefits. Analyzing users' browsing behaviors and preferences to provide online recommendations is an important trend for online news websites. In this research, we propose a novel online recommendation method for recommending movie articles to users when they are browsing the news. Specifically, association rule mining is conducted on user browsing news and movies to find the latent associations between news and movies. A novel online recommendation approach is proposed based on Latent Dirichlet Allocation, enhanced Collaborative Topic Modeling and the diversity of recommendations. We evaluate the proposed approach via an online evaluation on a real news website. The online evaluation results show that our proposed approach can enhance the click-through rate for recommending movie articles and alleviate the cold-start issue.
AB - Online news websites provide diverse article topics, such as fashion news, entertainment and movie articles to attract more users and create more benefits. Analyzing users' browsing behaviors and preferences to provide online recommendations is an important trend for online news websites. In this research, we propose a novel online recommendation method for recommending movie articles to users when they are browsing the news. Specifically, association rule mining is conducted on user browsing news and movies to find the latent associations between news and movies. A novel online recommendation approach is proposed based on Latent Dirichlet Allocation, enhanced Collaborative Topic Modeling and the diversity of recommendations. We evaluate the proposed approach via an online evaluation on a real news website. The online evaluation results show that our proposed approach can enhance the click-through rate for recommending movie articles and alleviate the cold-start issue.
KW - Collaborative Topic Modeling
KW - Diversity
KW - Latent Topic Model
KW - Online Recommendation
KW - Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85065205862&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2018.00013
DO - 10.1109/IIAI-AAI.2018.00013
M3 - Conference contribution
AN - SCOPUS:85065205862
T3 - Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
SP - 7
EP - 12
BT - Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
Y2 - 8 July 2018 through 13 July 2018
ER -