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20192023
most citedLightweight Self-Attentive Sequential Recommendation

111 citations · 225 across the 18 of their papers we have counts for

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Showing cs.IRShow all

11 papers · 1 filter

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.IR2021111 cited

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…

cs.IR202137 cited

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…

cs.IR2021

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…

cs.IR20211 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

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…