9 citations · 17 across the 14 of their papers we have counts for
15 papers
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.…
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…
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…
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…
Affiliation-based Local Community Detection across Multiple Networks
Li Ni, Zhou Xie, Yiwen Zhang +2
Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-doma…
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…
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)…
Spatial-Aware Local Community Detection Guided by Dominance Relation
Li Ni, Hefei Xu, Yiwen Zhang +1
The problem of finding the spatial-aware community for a given node has been defined and investigated in geo-social networks. However, existing studies suffer from two limitations:…