TY - GEN
T1 - DCAN
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
AU - Meng, Lingkang
AU - Shi, Chongyang
AU - Hao, Shufeng
AU - Su, Xiangrui
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Personalized news recommendation systems aim to alleviate information overload and provide users with personalized reading suggestions. In general, each news has its own lifecycle that is depicted by a bell-shaped curve of clicks, which is highly likely to influence users’ choices. However, existing methods typically depend on capturing user preference to make recommendations while ignoring the importance of news lifecycle. To fill this gap, we propose a Deep Co-Attention Network DCAN by modeling user preference and news lifecycle for news recommendation. The core of DCAN is a Co-Attention Net that fuses the user preference attention and news lifecycle attention together to model the dual influence of users’ clicked news. In addition, in order to learn the comprehensive news representation, a Multi-Path CNN is proposed to extract multiple patterns from the news title, content and entities. Moreover, to better capture user preference and model news lifecycle, we present a User Preference LSTM and a News Lifecycle LSTM to extract sequential correlations from news representations and additional features. Extensive experimental results on two real-world news datasets demonstrate the significant superiority of our method and validate the effectiveness of our Co-Attention Net by means of visualization.
AB - Personalized news recommendation systems aim to alleviate information overload and provide users with personalized reading suggestions. In general, each news has its own lifecycle that is depicted by a bell-shaped curve of clicks, which is highly likely to influence users’ choices. However, existing methods typically depend on capturing user preference to make recommendations while ignoring the importance of news lifecycle. To fill this gap, we propose a Deep Co-Attention Network DCAN by modeling user preference and news lifecycle for news recommendation. The core of DCAN is a Co-Attention Net that fuses the user preference attention and news lifecycle attention together to model the dual influence of users’ clicked news. In addition, in order to learn the comprehensive news representation, a Multi-Path CNN is proposed to extract multiple patterns from the news title, content and entities. Moreover, to better capture user preference and model news lifecycle, we present a User Preference LSTM and a News Lifecycle LSTM to extract sequential correlations from news representations and additional features. Extensive experimental results on two real-world news datasets demonstrate the significant superiority of our method and validate the effectiveness of our Co-Attention Net by means of visualization.
KW - Co-attention neural network
KW - Convolutional neural network
KW - News recommendation
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85104751323&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73200-4_7
DO - 10.1007/978-3-030-73200-4_7
M3 - Conference contribution
AN - SCOPUS:85104751323
SN - 9783030731991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 114
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Lee, Wang-Chien
A2 - Tseng, Vincent S.
A2 - Kalogeraki, Vana
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 April 2021 through 14 April 2021
ER -