Search Results for author: Kangyi Lin

Found 6 papers, 5 papers with code

GraphPro: Graph Pre-training and Prompt Learning for Recommendation

2 code implementations28 Nov 2023 Yuhao Yang, Lianghao Xia, Da Luo, Kangyi Lin, Chao Huang

The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.

Automated Self-Supervised Learning for Recommendation

2 code implementations14 Mar 2023 Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao

This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.

Collaborative Filtering Contrastive Learning +2

Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

1 code implementation21 Nov 2022 Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao

Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.

Click-Through Rate Prediction Knowledge Distillation +1

RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction

1 code implementation28 Oct 2022 Yanyan Shen, Lifan Zhao, Weiyu Cheng, Zibin Zhang, Wenwen Zhou, Kangyi Lin

Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.

Click-Through Rate Prediction Meta-Learning +2

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer

no code implementations25 Jan 2022 Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, Sophia Ananiadou

Although these methods have made great progress, they are often limited by the recommender system's direct exposure and inactive interactions, and thus fail to mine all potential user interests.

Click-Through Rate Prediction Representation Learning

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