49 citations · 87 across the 12 of their papers we have counts for
12 papers
Self-Supervised Multi-Modal Sequential Recommendation
Kunzhe Song, Qingfeng Sun, Can Xu +2
With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business reve…
Latent User Intent Modeling for Sequential Recommenders
Bo Chang, Alexandros Karatzoglou, Yuyan Wang +3
Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with base…
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…
LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation
Yin Zhang, Can Xu, XianJun Wu +3
Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities l…
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
Yufei Wang, Jiayi Zheng, Can Xu +4
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e…
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…
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Lingzhi Wang, Huang Hu, Lei Sha +3
Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS i…
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…
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
Wei Chen, Yeyun Gong, Can Xu +9
We study the problem of coarse-grained response selection in retrieval-based dialogue systems. The problem is equally important with fine-grained response selection, but is less ex…
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…