3.2k citations · 8.2k across the 71 of their papers we have counts for
72 papers
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…
Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang +4
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Lang…
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Xinyu Lin, Wenjie Wang, Jujia Zhao +3
Collaborative Filtering (CF) recommender models highly depend on user-item interactions to learn CF representations, thus falling short of recommending cold-start items. To address…
Attack Prompt Generation for Red Teaming and Defending Large Language Models
Boyi Deng, Wenjie Wang, Fuli Feng +3
Large language models (LLMs) are susceptible to red teaming attacks, which can induce LLMs to generate harmful content. Previous research constructs attack prompts via manual or au…
CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang +3
Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches…
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation
Xinyu Lin, Wenjie Wang, Yongqi Li +3
Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space…
RecAD: Towards A Unified Library for Recommender Attack and Defense
Changsheng Wang, Jianbai Ye, Wenjie Wang +3
In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social…
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…
A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
Keqin Bao, Jizhi Zhang, Wenjie Wang +6
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies, the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a cru…
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…
Recommendation Unlearning via Influence Function
Yang Zhang, Zhiyu Hu, Yimeng Bai +3
Recommendation unlearning is an emerging task to serve users for erasing unusable data (e.g., some historical behaviors) from a well-trained recommender model. Existing methods pro…
Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
Yang Zhang, Yimeng Bai, Jianxin Chang +6
With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recomme…
Robust Prompt Optimization for Large Language Models Against Distribution Shifts
Moxin Li, Wenjie Wang, Fuli Feng +3
Large Language Model (LLM) has demonstrated significant ability in various Natural Language Processing tasks. However, their effectiveness is highly dependent on the phrasing of th…
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…
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs
Fengbin Zhu, Chao Wang, Fuli Feng +3
Discrete reasoning over table-text documents (e.g., financial reports) gains increasing attention in recent two years. Existing works mostly simplify this challenge by manually sel…
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…
Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
Yulong Huang, Yang Zhang, Qifan Wang +2
Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans…
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…
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…
Causal Disentangled Recommendation Against User Preference Shifts
Wenjie Wang, Xinyu Lin, Liuhui Wang +3
Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has s…
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…
Mitigating Spurious Correlations for Self-supervised Recommendation
Xinyu Lin, Yiyan Xu, Wenjie Wang +2
Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correl…
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…