TY - GEN
T1 - News recommendation via multi-interest news sequence modelling
AU - Wang, Rongyao
AU - Wang, Shoujin
AU - Lu, Wenpeng
AU - Peng, Xueping
PY - 2022
Y1 - 2022
N2 - A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models. Our source code is publicly available on GitHub.
AB - A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session. Generally, a user's interests are diverse, namely there are multiple interests corresponding to different types of news, e.g., news of distinct topics, within a session. However, most of existing methods typically overlook such important characteristic and thus fail to distinguish and model the potential multiple interests of a user, impeding accurate recommendation of the next piece of news. Therefore, this paper proposes multi-interest news sequence (MINS) model for news recommendation. In MINS, a news encoder based on self-attention is devised on learn an informative embedding for each piece of news, and then a novel parallel interest network is devised to extract the potential multiple interests embedded in the news sequence in preparation for the subsequent next-news recommendations. The experimental results on a real-world dataset demonstrate that our model can achieve better performance than the state-of-the-art compared models. Our source code is publicly available on GitHub.
KW - News recommendation
KW - multi-interest modeling
KW - session-based recommendation
UR - http://www.scopus.com/inward/record.url?scp=85131248099&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747149
DO - 10.1109/ICASSP43922.2022.9747149
M3 - Conference proceeding contribution
AN - SCOPUS:85131248099
SN - 9781665405416
T3 - IEEE International Conference on Acoustics, Speech, and Signal Processing proceedings
SP - 7942
EP - 7946
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 22 May 2022 through 27 May 2022
ER -