45 citations · 139 across the 18 of their papers we have counts for
18 papers
Integrating Vision-Centric Text Understanding for Conversational Recommender Systems
Wei Yuan, Shutong Qiao, Tong Chen +3
Conversational Recommender Systems (CRSs) have attracted growing attention for their ability to deliver personalized recommendations through natural language interactions. To more…
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Wei Jiang, Tong Chen, Guanhua Ye +4
Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have…
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…
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…
Semi-decentralized Federated Ego Graph Learning for Recommendation
Liang Qu, Ningzhi Tang, Ruiqi Zheng +4
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as…
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin
Overfitting has long been considered a common issue to large neural network models in sequential recommendation. In our study, an interesting phenomenon is observed that overfittin…
Self-supervised Graph-based Point-of-interest Recommendation
Yang Li, Tong Chen, Peng-Fei Zhang +2
The exponential growth of Location-based Social Networks (LBSNs) has greatly stimulated the demand for precise location-based recommendation services. Next Point-of-Interest (POI)…
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…
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks
Mubashir Imran, Hongzhi Yin, Tong Chen +2
Modeling heterogeneity by extraction and exploitation of high-order information from heterogeneous information networks (HINs) has been attracting immense research attention in rec…
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…
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…
Learning to Ask Appropriate Questions in Conversational Recommendation
Xuhui Ren, Hongzhi Yin, Tong Chen +3
Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user pre…
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…
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
Tong Chen, Hongzhi Yin, Jie Ren +3
With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recent…
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…
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
Shijie Zhang, Hongzhi Yin, Tong Chen +3
In recent years, recommender system has become an indispensable function in all e-commerce platforms. The review rating data for a recommender system typically comes from open plat…
Enhancing Social Recommendation with Adversarial Graph Convolutional Networks
Junliang Yu, Hongzhi Yin, Jundong Li +3
Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent repor…