1 code implementation • 1 Nov 2023 • Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang
By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders.
no code implementations • 25 Oct 2023 • Qinyong Wang, Zhenxiang Gao, Rong Xu
Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs.
no code implementations • 3 Jul 2023 • Qinyong Wang, Zhenxiang Gao, Rong Xu
Initially, biomedical concepts are embedded using language models, and then embedding similarity is utilized to retrieve the top candidates.
1 code implementation • 27 Sep 2022 • Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.
1 code implementation • 23 Apr 2022 • Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Nguyen Quoc Viet Hung
Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
no code implementations • 2 Apr 2021 • Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, Xiangliang Zhang
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.
4 code implementations • 16 Jan 2021 • Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang
In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.
2 code implementations • 12 Dec 2020 • Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
no code implementations • 2 Oct 2020 • Hongzhi Yin, Qinyong Wang, Kai Zheng, Zhixu Li, Xiaofang Zhou
Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the limitation and sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members.
no code implementations • 8 Sep 2019 • Junliang Yu, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, Qinyong Wang
Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems.