no code implementations • 11 Mar 2024 • Weiqing Luo, Chonggang Song, Lingling Yi, Gong Cheng
The utilization of semantic information is an important research problem in the field of recommender systems, which aims to complement the missing parts of mainstream ID-based approaches.
no code implementations • 28 Aug 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Nian Li, Lingling Yi, Depeng Jin, Yong Li
Micro-videos platforms such as TikTok are extremely popular nowadays.
1 code implementation • 12 Jul 2023 • Yan Wen, Chen Gao, Lingling Yi, Liwei Qiu, Yaqing Wang, Yong Li
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner.
1 code implementation • 15 Mar 2023 • Yuhan Quan, Jingtao Ding, Chen Gao, Lingling Yi, Depeng Jin, Yong Li
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations.
1 code implementation • 10 Aug 2022 • Yu Zheng, Chen Gao, Jingtao Ding, Lingling Yi, Depeng Jin, Yong Li, Meng Wang
Recommender systems are prone to be misled by biases in the data.
1 code implementation • 13 May 2022 • Xiangnan He, Yang Zhang, Fuli Feng, Chonggang Song, Lingling Yi, Guohui Ling, Yongdong Zhang
We demonstrate DCR on the backbone model of neural factorization machine (NFM), showing that DCR leads to more accurate prediction of user preference with small inference time cost.
no code implementations • 14 Jun 2021 • Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang
Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules.
no code implementations • 9 May 2020 • Qiaoan Chen, Hao Gu, Lingling Yi, Yishi Lin, Peng He, Chuan Chen, Yangqiu Song
Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.