TY - GEN
T1 - TideFC
T2 - 2020 International Computer Symposium, ICS 2020
AU - Tsai, Chang Ming
AU - Li, Cheng Te
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Recommendation system is becoming more and more important. Specially in social network, such as Twitter, Facebook, and YouTube. In the beginning, a number of studies learn the relationship of users and items from a bipartite graph. They embed each user and item in an embedding space. However, they ignore temporal properties. They regard users and items as static embeddings. As time passed, user preferences and item concepts can change, and node embeddings should be dynamically adjusted in the embedding space. Nevertheless, most of existing models update embeddings only when user takes an interaction with an item. In this paper, we propose TideFC, a novel model based on the state-of-the-art dynamic embedding model JODIE. TideFC can predict the future trajectories of users' and items' embeddings. Besides, we take advantage of t-batch that creates time-consistent batches to make the training stage more efficient. More importantly, we incorporate feature crossing to generate high-order feature interactions in our TideFC. Experiments conducted on multiple real datasets demonstrate the promising performance of TideFC, compared with the state-of-the-art JODIE.
AB - Recommendation system is becoming more and more important. Specially in social network, such as Twitter, Facebook, and YouTube. In the beginning, a number of studies learn the relationship of users and items from a bipartite graph. They embed each user and item in an embedding space. However, they ignore temporal properties. They regard users and items as static embeddings. As time passed, user preferences and item concepts can change, and node embeddings should be dynamically adjusted in the embedding space. Nevertheless, most of existing models update embeddings only when user takes an interaction with an item. In this paper, we propose TideFC, a novel model based on the state-of-the-art dynamic embedding model JODIE. TideFC can predict the future trajectories of users' and items' embeddings. Besides, we take advantage of t-batch that creates time-consistent batches to make the training stage more efficient. More importantly, we incorporate feature crossing to generate high-order feature interactions in our TideFC. Experiments conducted on multiple real datasets demonstrate the promising performance of TideFC, compared with the state-of-the-art JODIE.
UR - http://www.scopus.com/inward/record.url?scp=85102190424&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102190424&partnerID=8YFLogxK
U2 - 10.1109/ICS51289.2020.00115
DO - 10.1109/ICS51289.2020.00115
M3 - Conference contribution
AN - SCOPUS:85102190424
T3 - Proceedings - 2020 International Computer Symposium, ICS 2020
SP - 565
EP - 569
BT - Proceedings - 2020 International Computer Symposium, ICS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 December 2020 through 19 December 2020
ER -