9 citations · 17 across the 22 of their papers we have counts for
23 papers
UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
Honghao Li, Xianquan Wang, Zibin Zhang +3
Ranking is a core stage in online advertising and recommender systems. Modern ranking models increasingly unify sequential modeling and feature interaction, yet many advances rely…
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
Yi Zhang, Yiwen Zhang, Kai Zheng +2
The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback.…
FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost
Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade +17
Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictio…
Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
Sijia Li, Min Gao, Zongwei Wang +3
Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns. Transformer-based recommenders demons…
MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video
Haoyu Zhu, Yi Zhang, Lei Yao +2
Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge due to the limitations in capturing high-resolution geometry, hand-object interactions, a…
Efficient Sequential Recommendation for Long Term User Interest Via Personalization
Qiang Zhang, Hanchao Yu, Ivan Ji +14
Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for seque…
ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation
Yi Zhang, Yiwen Zhang, Yu Wang +2
The powerful text understanding and generation capabilities of large language models (LLMs) have brought new vitality to general recommendation with implicit feedback. One possible…
Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation
Yu Wang, Yonghui Yang, Le Wu +3
Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendation by enabling natural language reasoning over user behavior sequences. A common…
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…
Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation
Yi Zhang, Yiwen Zhang, Yu Wang +2
Generative recommendation aims to learn the underlying generative process over the entire item set to produce recommendations for users. Although it leverages non-linear probabilis…
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…
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems
Yi Zhang, Yiwen Zhang
The core of the general recommender systems lies in learning high-quality embedding representations of users and items to investigate their positional relations in the feature spac…
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…