TY - GEN
T1 - Variational recurrent model for session-based recommendation
AU - Wang, Zhitao
AU - Chen, Chengyao
AU - Zhang, Ke
AU - Lei, Yu
AU - Li, Wenjie
PY - 2018/10/17
Y1 - 2018/10/17
N2 - Session-based recommendation performance has been significantly improved by Recurrent Neural Networks (RNN). However, existing RNN-based models do not expose the global knowledge of frequent click patterns or consider variability of sequential behaviors in sessions. In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling. A stochastic generative process of session sequence is specified, where the latent variable modulates the generation of session sequences in RNN. We further extend VRM to a Conditional Variational Recurrent Model (CVRM) by considering additional information (e.g., focused category in sessions) as the generative condition. When evaluated on a public benchmark dataset, VRM and its extension clearly demonstrate their superiority over popular baselines and state-of-the-art models.
AB - Session-based recommendation performance has been significantly improved by Recurrent Neural Networks (RNN). However, existing RNN-based models do not expose the global knowledge of frequent click patterns or consider variability of sequential behaviors in sessions. In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling. A stochastic generative process of session sequence is specified, where the latent variable modulates the generation of session sequences in RNN. We further extend VRM to a Conditional Variational Recurrent Model (CVRM) by considering additional information (e.g., focused category in sessions) as the generative condition. When evaluated on a public benchmark dataset, VRM and its extension clearly demonstrate their superiority over popular baselines and state-of-the-art models.
KW - Latent Variational Model
KW - Recurrent Neural Network
KW - Session-based Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85058008516&partnerID=8YFLogxK
U2 - 10.1145/3269206.3269302
DO - 10.1145/3269206.3269302
M3 - Conference article published in proceeding or book
AN - SCOPUS:85058008516
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1839
EP - 1842
BT - CIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery
T2 - 27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Y2 - 22 October 2018 through 26 October 2018
ER -