440 citations · 1.1k across the 26 of their papers we have counts for
27 papers
VIP5: Towards Multimodal Foundation Models for Recommendation
Shijie Geng, Juntao Tan, Shuchang Liu +2
Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems (RecSys) are three prominent AI applications that have traditionally developed independently, resul…
Dynamic Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Zuohui Fu +3
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loo…
A Survey on Trustworthy Recommender Systems
Yingqiang Ge, Shuchang Liu, Zuohui Fu +6
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. How…
Explainable Fairness in Recommendation
Yingqiang Ge, Juntao Tan, Yan Zhu +7
Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies…
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
Shijie Geng, Shuchang Liu, Zuohui Fu +2
For a long time, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned kno…
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
Juntao Tan, Shijie Geng, Zuohui Fu +4
Structural data well exists in Web applications, such as social networks in social media, citation networks in academic websites, and threads data in online forums. Due to the comp…
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Honglu Zhou, Advith Chegu, Samuel S. Sohn +3
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed…
Dense Contrastive Visual-Linguistic Pretraining
Lei Shi, Kai Shuang, Shijie Geng +5
Inspired by the success of BERT, several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior p…
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu +2
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects…
Faithfully Explainable Recommendation via Neural Logic Reasoning
Yaxin Zhu, Yikun Xian, Zuohui Fu +2
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation pr…
Efficient Non-Sampling Knowledge Graph Embedding
Zelong Li, Jianchao Ji, Zuohui Fu +4
Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negat…
Context-Aware Interaction Network for Question Matching
Zhe Hu, Zuohui Fu, Yu Yin +1
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. Ho…
Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li +3
Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlat…
RomeBERT: Robust Training of Multi-Exit BERT
Shijie Geng, Peng Gao, Zuohui Fu +1
BERT has achieved superior performances on Natural Language Understanding (NLU) tasks. However, BERT possesses a large number of parameters and demands certain resources to deploy.…
Discrete Knowledge Graph Embedding based on Discrete Optimization
Yunqi Li, Shuyuan Xu, Bo Liu +4
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable di…
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Yikun Xian, Zuohui Fu, Handong Zhao +8
Recent research explores incorporating knowledge graphs (KG) into e-commerce recommender systems, not only to achieve better recommendation performance, but more importantly to gen…
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
Zuohui Fu, Yikun Xian, Yaxin Zhu +2
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE. The dataset is constructed from an Amazon revi…
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
Xin Dong, Yaxin Zhu, Yupeng Zhang +4
In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different la…
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
Yikun Xian, Zuohui Fu, Qiaoying Huang +2
Recent work on recommender systems has considered external knowledge graphs as valuable sources of information, not only to produce better recommendations but also to provide expla…
Contrastive Visual-Linguistic Pretraining
Lei Shi, Kai Shuang, Shijie Geng +6
Several multi-modality representation learning approaches such as LXMERT and ViLBERT have been proposed recently. Such approaches can achieve superior performance due to the high-l…
Learning Personalized Risk Preferences for Recommendation
Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3
The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…
Fairness-Aware Explainable Recommendation over Knowledge Graphs
Zuohui Fu, Yikun Xian, Ruoyuan Gao +8
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. Explainable recommendation systems, in parti…
Learning Post-Hoc Causal Explanations for Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu +3
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of blac…
Character Matters: Video Story Understanding with Character-Aware Relations
Shijie Geng, Ji Zhang, Zuohui Fu +3
Different from short videos and GIFs, video stories contain clear plots and lists of principal characters. Without identifying the connection between appearing people and character…