@Article{cmc.2020.09907,
AUTHOR = {Kehua Yang, Shaosong Long, Wei Zhang, Jiqing Yao, Jing Liu},
TITLE = {Personalized News Recommendation Based on the Text and Image Integration},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {557--570},
URL = {http://www.techscience.com/cmc/v64n1/39158},
ISSN = {1546-2226},
ABSTRACT = {The personalized news recommendation has been very popular in the news 
recommendation field. In most research, the picture information in the news is ignored, but 
the information conveyed to the users through pictures is more intuitive and more likely to 
affect the users’ reading interests than the one in the textual form. Therefore, in this paper, a 
model that combines images and texts in the news is proposed. In this model, the new tags 
are extracted from the images and texts in the news, and based on these new tags, an adaptive 
tag (AT) algorithm is proposed. The AT algorithm selects the tags the user is interested in 
based on the user feedback. In particular, the AT algorithm can predict tags that a user may 
be interested in with the help of the tag correlation graph without any user feedback. The 
proposed AT algorithm is verified by experiments. The experimental results verified the AT 
algorithm regarding three evaluation indexes F1-score (F1), area under curve (AUC) and 
mean reciprocal rank (MRR). The recommended effect of the proposed algorithm is found 
to be better than those of the various baseline algorithms on real-world datasets.},
DOI = {10.32604/cmc.2020.09907}
}