Search Results for author: Jibing Gong

Found 6 papers, 3 papers with code

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

1 code implementation28 Jun 2023 Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu

To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.

Collaborative Filtering

MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System

1 code implementation14 Nov 2022 Liangwei Yang, Shen Wang, Jibing Gong, Shaojie Zheng, Shuying Du, Zhiwei Liu, Philip S. Yu

To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).

Recommendation Systems

Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

no code implementations8 Mar 2022 Jibing Gong, Yao Wan, Ye Liu, Xuewen Li, Yi Zhao, Cheng Wang, YuTing Lin, Xiaohan Fang, Wenzheng Feng, Jingyi Zhang, Jie Tang

Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise.

Graph Attention reinforcement-learning +1

Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View

2 code implementations23 Jun 2020 Shen Wang, Jibing Gong, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu

To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.

Representation Learning

Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling

no code implementations6 Oct 2019 Hekai Zhang, Jibing Gong, Zhiyong Teng, Dan Wang, Hongfei Wang, Linfeng Du, Zakirul Alam Bhuiyan

Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects.

Attribute

W-RNN: News text classification based on a Weighted RNN

no code implementations28 Sep 2019 Dan Wang, Jibing Gong, Yaxi Song

For the problem that the feature high dimensionality and unclear semantic relationship in text data representation, we first utilize the word vector to represent the vocabulary in the text and use Recurrent Neural Network (RNN) to extract features of the serialized text data.

General Classification text-classification +1

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