96 citations · 245 across the 13 of their papers we have counts for
13 papers
Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation
Yidan Hu, Yong Liu, Chunyan Miao +2
Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods…
Geometry-Entangled Visual Semantic Transformer for Image Captioning
Ling Cheng, Wei Wei, Feida Zhu +2
Recent advancements of image captioning have featured Visual-Semantic Fusion or Geometry-Aid attention refinement. However, those fusion-based models, they are still criticized for…
A Survey on Reinforcement Learning for Recommender Systems
Yuanguo Lin, Yong Liu, Fan Lin +5
Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender syste…
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Xin Zhou, Aixin Sun, Yong Liu +2
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt nega…
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
Yinan Zhang, Boyang Li, Yong Liu +2
Proper initialization is crucial to the optimization and the generalization of neural networks. However, most existing neural recommendation systems initialize the user and item em…
KECRS: Towards Knowledge-Enriched Conversational Recommendation System
Tong Zhang, Yong Liu, Peixiang Zhong +3
The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. To better understand user's intentions,…
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
Chenyi Lei, Shixian Luo, Yong Liu +6
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content. However, it is still very challenging to pre-train neural models f…
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Yong Liu, Susen Yang, Chenyi Lei +5
Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training…
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
Susen Yang, Yong Liu, Yonghui Xu +3
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an ent…
Learning Hierarchical Review Graph Representations for Recommendation
Yong Liu, Susen Yang, Yinan Zhang +3
The user review data have been demonstrated to be effective in solving different recommendation problems. Previous review-based recommendation methods usually employ sophisticated…
Bandit Learning for Diversified Interactive Recommendation
Yong Liu, Yingtai Xiao, Qiong Wu +2
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus o…
Recent Advances in Diversified Recommendation
Qiong Wu, Yong Liu, Chunyan Miao +3
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not. In recen…
Multi-Scale Quasi-RNN for Next Item Recommendation
Chaoyue He, Yong Liu, Qingyu Guo +1
How to better utilize sequential information has been extensively studied in the setting of recommender systems. To this end, architectural inductive biases such as Markov-Chains,…