TY - JOUR
T1 - Online Product Recommendations based on Diversity and Latent Association Analysis on News and Products
AU - Chen, Hsing Yu
AU - Lin, Yu Chun
AU - Liu, Duen Ren
AU - Liu, Tzeng Feng
N1 - Publisher Copyright:
© 2022 Institute of Information Science. All rights reserved.
PY - 2022/9
Y1 - 2022/9
N2 - Integrating news websites with product recommendation can create more benefit and is an important trend of online worlds. The information offered by the websites is becoming even more complicated. Accordingly, it is important for the websites to implement online recommendation methods that can raise the users’ click-through rates and loyalty. In this work, we proposed a novel online product recommendation approach for recommending products during news browsing. The proposed method combines online hybrid interest analysis and recommendation diversity. There are cold-start and data sparsity issues on the website. Accordingly, a hybrid of collaborative filtering and content-based approach is used to alleviate the issues. Specifically, latent association analysis is conducted on user browsing news and products to discover the latent associations between products and news. Moreover, a hybrid method is proposed based on Matrix Factorization and Latent Topic Modeling to predict user preferences for products. In addition, online interest analysis is integrated to adjust users’ online product interests according to the currently browsing news. Finally, the proposed approach combines recommendation diversity and users’ online interests to raise the chance of discovering potential user preferences on products and enhance the click through rate of online product recommendations. Online evaluations are conducted on a news website to evaluate the proposed approach. Our online experimental results indicate that the proposed approach can enhance the click-through rate of online product recommendations.
AB - Integrating news websites with product recommendation can create more benefit and is an important trend of online worlds. The information offered by the websites is becoming even more complicated. Accordingly, it is important for the websites to implement online recommendation methods that can raise the users’ click-through rates and loyalty. In this work, we proposed a novel online product recommendation approach for recommending products during news browsing. The proposed method combines online hybrid interest analysis and recommendation diversity. There are cold-start and data sparsity issues on the website. Accordingly, a hybrid of collaborative filtering and content-based approach is used to alleviate the issues. Specifically, latent association analysis is conducted on user browsing news and products to discover the latent associations between products and news. Moreover, a hybrid method is proposed based on Matrix Factorization and Latent Topic Modeling to predict user preferences for products. In addition, online interest analysis is integrated to adjust users’ online product interests according to the currently browsing news. Finally, the proposed approach combines recommendation diversity and users’ online interests to raise the chance of discovering potential user preferences on products and enhance the click through rate of online product recommendations. Online evaluations are conducted on a news website to evaluate the proposed approach. Our online experimental results indicate that the proposed approach can enhance the click-through rate of online product recommendations.
KW - diversity
KW - latent topic modeling
KW - matrix factorization
KW - online recommendation
KW - product recommendation
UR - http://www.scopus.com/inward/record.url?scp=85138441922&partnerID=8YFLogxK
U2 - 10.6688/JISE.202209_38(5).0012
DO - 10.6688/JISE.202209_38(5).0012
M3 - Article
AN - SCOPUS:85138441922
SN - 1016-2364
VL - 38
SP - 1065
EP - 1085
JO - Journal of Information Science and Engineering
JF - Journal of Information Science and Engineering
IS - 5
ER -