49 citations · 56 across the 6 of their papers we have counts for
6 papers
Reward Shaping for User Satisfaction in a REINFORCE Recommender
Konstantina Christakopoulou, Can Xu, Sai Zhang +10
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key…
Recency Dropout for Recurrent Recommender Systems
Bo Chang, Can Xu, Matthieu Lê +5
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories. However, recurrent neural networks (RNNs) are known to have d…
Learning Neural Templates for Recommender Dialogue System
Zujie Liang, Huang Hu, Can Xu +6
Though recent end-to-end neural models have shown promising progress on Conversational Recommender System (CRS), two key challenges still remain. First, the recommended items canno…
Maria: A Visual Experience Powered Conversational Agent
Zujie Liang, Huang Hu, Can Xu +5
Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus pro…
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling +3
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification…
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Jiaxi Tang, Francois Belletti, Sagar Jain +4
Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and ite…