111 citations · 801 across the 59 of their papers we have counts for
12 papers · 2 filters
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Hongzhi Yin, Xin Xia +3
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is…
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion
Shijie Zhang, Hongzhi Yin, Tong Chen +3
Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models a…
Lightweight Self-Attentive Sequential Recommendation
Yang Li, Tong Chen, Peng-Fei Zhang +1
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various se…
Exploiting Positional Information for Session-based Recommendation
Ruihong Qiu, Zi Huang, Tong Chen +1
For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect…
Attribute-aware Explainable Complementary Clothing Recommendation
Yang Li, Tong Chen, Zi Huang
Modelling mix-and-match relationships among fashion items has become increasingly demanding yet challenging for modern E-commerce recommender systems. When performing clothes match…
Learning Elastic Embeddings for Customizing On-Device Recommenders
Tong Chen, Hongzhi Yin, Yujia Zheng +3
In today's context, deploying data-driven services like recommendation on edge devices instead of cloud servers becomes increasingly attractive due to privacy and network latency c…
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Yang Li, Tong Chen, Yadan Luo +2
Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting hist…
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Lei Guo, Li Tang, Tong Chen +3
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a…
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
Tong Chen, Hongzhi Yin, Xiangliang Zhang +3
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manu…
Fast-adapting and Privacy-preserving Federated Recommender System
Qinyong Wang, Hongzhi Yin, Tong Chen +3
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem. Rece…
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
Lei Guo, Hongzhi Yin, Tong Chen +2
In this work, we study group recommendation in a particular scenario, namely Occasional Group Recommendation (OGR). Most existing works have addressed OGR by aggregating group memb…
Graph Embedding for Recommendation against Attribute Inference Attacks
Shijie Zhang, Hongzhi Yin, Tong Chen +3
In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be natur…