3.2k citations · 18.4k across the 142 of their papers we have counts for
143 papers
Information Discovery in e-Commerce
Zhaochun Ren, Xiangnan He, Dawei Yin +1
Electronic commerce, or e-commerce, is the buying and selling of goods and services, or the transmitting of funds or data online. E-commerce platforms come in many kinds, with glob…
Knowledge Graph Embedding by Normalizing Flows
Changyi Xiao, Xiangnan He, Yixin Cao
A key to knowledge graph embedding (KGE) is to choose a proper representation space, e.g., point-wise Euclidean space and complex vector space. In this paper, we propose a unified…
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Xiang Wang, Xiangnan He +3
Graph anomaly detection (GAD) is a challenging binary classification problem due to its different structural distribution between anomalies and normal nodes -- abnormal nodes are a…
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Jiarui Yu, Haoran Li, Yanbin Hao +3
The large-scale visual-language pre-trained model, Contrastive Language-Image Pre-training (CLIP), has significantly improved image captioning for scenarios without human-annotated…
ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction
Haoxuan Li, Taojun Hu, Zetong Xiong +4
Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies…
Label Denoising through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +2
Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Vague Preference Policy Learning for Conversational Recommendation
Gangyi Zhang, Chongming Gao, Wenqiang Lei +6
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit…
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
Jizhi Zhang, Keqin Bao, Yang Zhang +3
The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm -- Recommendation via LLM (RecLLM). Nevertheless, it is imp…
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
Keqin Bao, Jizhi Zhang, Yang Zhang +3
Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation sys…
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
Yang Zhang, Tianhao Shi, Fuli Feng +4
Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is lear…
GIF: A General Graph Unlearning Strategy via Influence Function
Jiancan Wu, Yi Yang, Yuchun Qian +3
With the greater emphasis on privacy and security in our society, the problem of graph unlearning -- revoking the influence of specific data on the trained GNN model, is drawing in…
Diffusion Recommender Model
Wenjie Wang, Yiyan Xu, Fuli Feng +3
Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) are widely utilized to model the generative process of user interactions. Howe…
Generative Recommendation: Towards Next-generation Recommender Paradigm
Wenjie Wang, Xinyu Lin, Fuli Feng +2
Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) th…
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
Zhicai Wang, Yanbin Hao, Tingting Mu +3
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not mat…
On the Theories Behind Hard Negative Sampling for Recommendation
Wentao Shi, Jiawei Chen, Fuli Feng +4
Negative sampling has been heavily used to train recommender models on large-scale data, wherein sampling hard examples usually not only accelerates the convergence but also improv…
Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors
Keqin Bao, Yu Wan, Dayiheng Liu +5
Fine-grained information on translation errors is helpful for the translation evaluation community. Existing approaches can not synchronously consider error position and type, fail…
Weakly Supervised Anomaly Detection: A Survey
Minqi Jiang, Chaochuan Hou, Ao Zheng +6
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.…
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Wentao Shi, Junkang Wu, Xuezhi Cao +4
Learning hyperbolic embeddings for knowledge graph (KG) has gained increasing attention due to its superiority in capturing hierarchies. However, some important operations in hyper…
Adap-: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu +3
Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these meth…
Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng +2
As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first tra…
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
Yongduo Sui, Qitian Wu, Jiancan Wu +5
The issue of distribution shifts is emerging as a critical concern in graph representation learning. From the perspective of invariant learning and stable learning, a recently well…