111 citations · 801 across the 59 of their papers we have counts for
33 papers · 1 filter
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Hung Vinh Tran, Tong Chen, Quoc Viet Hung Nguyen +3
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical feature…
Lightweight Embeddings for Graph Collaborative Filtering
Xurong Liang, Tong Chen, Lizhen Cui +3
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item…
On-Device Recommender Systems: A Tutorial on The New-Generation Recommendation Paradigm
Hongzhi Yin, Tong Chen, Liang Qu +1
Given the sheer volume of contemporary e-commerce applications, recommender systems (RSs) have gained significant attention in both academia and industry. However, traditional clou…
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Lijian Chen, Wei Yuan, Tong Chen +3
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. Whi…
Budgeted Embedding Table For Recommender Systems
Yunke Qu, Tong Chen, Quoc Viet Hung Nguyen +1
At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which a…
Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation
Xurong Liang, Tong Chen, Quoc Viet Hung Nguyen +2
Latent factor models are the dominant backbones of contemporary recommender systems (RSs) given their performance advantages, where a unique vector embedding with a fixed dimension…
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Yi Liu, Hongrui Xuan, Bohan Li +3
Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (…
Personalized Elastic Embedding Learning for On-Device Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact rec…
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen +3
Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests an…
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Xuhui Ren, Wei Yuan, Tong Chen +3
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-ba…
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +3
As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and inte…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Junliang Yu, Xin Xia, Tong Chen +3
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure t…
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
Mubashir Imran, Hongzhi Yin, Tong Chen +3
Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent re…
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Lei Guo, Jinyu Zhang, Tong Chen +2
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics i…
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Lei Guo, Jinyu Zhang, Li Tang +3
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense…
Decentralized Collaborative Learning Framework for Next POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +1
Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide th…
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Tong Chen, Hongzhi Yin, Jing Long +3
As a step beyond traditional personalized recommendation, group recommendation is the task of suggesting items that can satisfy a group of users. In group recommendation, the core…
Self-Supervised Learning for Recommender Systems: A Survey
Junliang Yu, Hongzhi Yin, Xin Xia +3
In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self…
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…