activity
20202026
most citedLearning to Ask Appropriate Questions in Conversational Recommendation

45 citations · 139 across the 18 of their papers we have counts for

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11 papers · 1 filter

cs.IR2026

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…

cs.IR2024★ 7 cited

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…

cs.IR2023★ 3 cited

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…

cs.IR2022

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…

cs.IR2022★ 35 cited

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…

cs.IR2021

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…

cs.IR2021★ 1 cited

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…

cs.IR2021★ 8 cited

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…

cs.IR2021

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…

cs.IR2021★ 5 cited

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…

cs.IR2020★ 17 cited

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…