5 citations · 8 across the 20 of their papers we have counts for
21 papers
Bezier-Guided Adaptive Flow for Cross-domain Sequential Recommendation
Ziang Lu, Yu Zhang, Xinzhe Jiang +2
Cross-domain sequential recommendation aims to alleviate data sparsity and cold-start problems by transferring useful preference knowledge across domains. However, users exhibit va…
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation
Lin Mu, Guoji Wang, Li Ni +4
Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities. However, effectively aligning the…
DIAURec: Dual-Intent Space Representation Optimization for Recommendation
Yu Zhang, Yiwen Zhang, Yi Zhang +1
General recommender systems deliver personalized services by learning user and item representations, with the central challenge being how to capture latent user preferences. Howeve…
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models
Lin Mu, Haiyang Wang, Li Ni +4
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions further enhance flexibility by dy…
From Clues to Generation: Language-Guided Conditional Diffusion for Cross-Domain Recommendation
Ziang Lu, Lei Sang, Lin Mu +1
Cross-domain Recommendation (CDR) exploits multi-domain correlations to alleviate data sparsity. As a core task within this field, inter-domain recommendation focuses on predicting…
Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks
Lin Mu, Guowei Chu, Li Ni +2
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to inpu…
Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation
Lei Sang, Yu Wang, Yiwen Zhang
Heterogeneous graph neural networks (HGNNs) have demonstrated their superiority in exploiting auxiliary information for recommendation tasks. However, graphs constructed using meta…
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +2
Hadamard Product (HP) has long been a cornerstone in click-through rate (CTR) prediction tasks due to its simplicity, effectiveness, and ability to capture feature interactions wit…
Quadratic Interest Network for Multimodal Click-Through Rate Prediction
Honghao Li, Hanwei Li, Jing Zhang +4
Multimodal click-through rate (CTR) prediction is a key technique in industrial recommender systems. It leverages heterogeneous modalities such as text, images, and behavioral logs…
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Yu Zhang, Yiwen Zhang, Yi Zhang +2
Personalized recommendation is widely used in the web applications, and graph contrastive learning (GCL) has gradually become a dominant approach in recommender systems, primarily…
Intent Representation Learning with Large Language Model for Recommendation
Yu Wang, Lei Sang, Yi Zhang +1
Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for…
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
Lei Sang, Qiuze Ru, Honghao Li +3
Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions thr…
From Collapse to Stability: A Knowledge-Driven Ensemble Framework for Scaling Up Click-Through Rate Prediction Models
Honghao Li, Lei Sang, Yi Zhang +2
Click-through rate (CTR) prediction plays a crucial role in modern recommender systems. While many existing methods utilize ensemble networks to improve CTR model performance, they…
Intent-guided Heterogeneous Graph Contrastive Learning for Recommendation
Lei Sang, Yu Wang, Yi Zhang +2
Contrastive Learning (CL)-based recommender systems have gained prominence in the context of Heterogeneous Graph (HG) due to their capacity to enhance the consistency of representa…
Adaptive Fusion Self-supervised Learning for Recommendation
Yu Zhang, Lei Sang, Yi Zhang +2
Self-supervised learning (SSL) has recently attracted significant attention in the field of recommender systems. Contrastive learning (CL) stands out as a major SSL paradigm due to…
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +3
As an important modeling paradigm in click-through rate (CTR) prediction, the Deep & Cross Network (DCN) and its derivative models have gained widespread recognition primarily due…
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative Filtering
Yi Zhang, Lei Sang, Yiwen Zhang
Intent modeling has attracted widespread attention in recommender systems. As the core motivation behind user selection of items, intent is crucial for elucidating recommendation r…
Dual-domain Collaborative Denoising for Social Recommendation
Wenjie Chen, Yi Zhang, Honghao Li +2
Social recommendation leverages social network to complement user-item interaction data for recommendation task, aiming to mitigate the data sparsity issue in recommender systems.…
TF4CTR: Twin Focus Framework for CTR Prediction via Adaptive Sample Differentiation
Honghao Li, Qiuze Ru, Yiwen Zhang +3
Effective feature interaction modeling is critical for enhancing the accuracy of click-through rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR…
Generative-Contrastive Heterogeneous Graph Neural Network
Yu Wang, Lei Sang, Yi Zhang +2
Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL)…
CETN: Contrast-enhanced Through Network for CTR Prediction
Honghao Li, Lei Sang, Yi Zhang +2
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search. Most exist…