Search Results for author: Liangcai Su

Found 7 papers, 4 papers with code

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance

1 code implementation13 Dec 2023 Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu

To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE).

Denoising Node Classification +1

Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation

no code implementations30 Nov 2023 Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang

Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.

Retrieval

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation

1 code implementation16 Aug 2023 Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang

Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.

Multi-Task Learning Recommendation Systems

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

4 code implementations3 Apr 2023 Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong

As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.

Click-Through Rate Prediction feature selection +1

BARS: Towards Open Benchmarking for Recommender Systems

5 code implementations19 May 2022 Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang

Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.

Benchmarking Recommendation Systems

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

no code implementations23 Mar 2022 Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He

Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.

Recommendation Systems Re-Ranking

DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction

no code implementations19 Oct 2021 He Li, Shiyu Zhang, Xuejiao Li, Liangcai Su, Hongjie Huang, Duo Jin, Linghao Chen, Jianbing Huang, Jaesoo Yoo

Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion, but utilizing these data presents unique challenges including: the dynamic temporal correlation, and the dynamic spatial correlation caused by changes in road conditions.

Traffic Prediction

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