Filters








11,782 Hits in 6.2 sec

Session-based Recommendation with Graph Neural Networks [article]

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
2019 arXiv   pre-print
Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graph-structured data.  ...  The problem of session-based recommendation aims to predict user actions based on anonymous sessions.  ...  Graph neural networks are well-suited for session-based recommendation, because it can automatically extract features of session graphs with considerations of rich node connections.  ... 
arXiv:1811.00855v4 fatcat:z7z7pf6skvc7jeocgd2lelekku

Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints [article]

Minjae Park
2022 arXiv   pre-print
In this paper, we consider various relationships in graph created by sessions through Heterogeneous attention network.  ...  Session-based recommendation, a sub-area of recommender systems, attempts to recommend items by interpreting sessions that consist of sequences of items.  ...  METHODOLOGY Overview This section details the design of heterogeneous graph neural network model for personalized session-based recommendations with user-session constraints.  ... 
arXiv:2205.11343v3 fatcat:hhpl46kvgrgbhfrypeebie6z3q

Session-Based Recommendation with Graph Neural Networks

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data.  ...  The problem of session-based recommendation aims to predict user actions based on anonymous sessions.  ...  Graph neural networks are well-suited for session-based recommendation, because it can automatically extract features of session graphs with considerations of rich node connections.  ... 
doi:10.1609/aaai.v33i01.3301346 fatcat:x5bhyl5b3jewrbgiocuhvbkh2e

Personalized Graph Neural Networks with Attention Mechanism for Session-Aware Recommendation [article]

Shu Wu, Mengqi Zhang, Xin Jiang, Ke Xu, Liang Wang
2020 arXiv   pre-print
To this end, we propose a novel method, named Personalized Graph Neural Networks with Attention Mechanism (A-PGNN) for brevity.  ...  The problem of session-aware recommendation aims to predict users' next click based on their current session and historical sessions.  ...  Graph neural networks Nowadays, neural network has been used for generating representation for graph-structured data, for example, social network and knowledge bases.  ... 
arXiv:1910.08887v3 fatcat:jkkiqvthtbghlpaqlq7556crte

Session-based Recommendation with Heterogeneous Graph Neural Network [article]

Jinpeng Chen, Haiyang Li, Fan Zhang, Senzhang Wang, Kaimin Wei
2021 arXiv   pre-print
In this paper, we propose a heterogeneous graph neural network-based session recommendation method, named SR-HetGNN, which can learn session embeddings by heterogeneous graph neural network (HetGNN), and  ...  The purpose of the Session-Based Recommendation System is to predict the user's next click according to the previous session sequence.  ...  Session-Based Recommendation with Heterogeneous Graph Neural Networks in detail; Section 5 summarizes this paper.  ... 
arXiv:2108.05641v1 fatcat:zid77tubpncejp3kyh43ptlwi4

Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks

Ruihong Qiu, Zi Huang, Jingjing Li, Hongzhi Yin
2020 ACM Transactions on Information Systems (TOIS; Formerly: ACM Transactions on Office Information Systems)  
In this paper, we solve these problems with the graph neural networks technique.  ...  First, each session is represented as a graph rather than a linear sequence structure, based on which a novel Full Graph Neural Network (FGNN) is proposed to learn complicated item dependency.  ...  With the prevalence of deep learning, neural networks are widely used.  ... 
doi:10.1145/3382764 fatcat:byzvhh4eenbebg7f7qdhr7wrme

Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks

Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi YIn
2019 Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19  
graph for a session-based recommender system.  ...  We formulate the next item recommendation within the session as a graph classification problem.  ...  To utilize graph neural networks, we build a session graph for every session and formulate the recommendation as a graph classification problem.  ... 
doi:10.1145/3357384.3358010 dblp:conf/cikm/QiuLHY19 fatcat:rnmyt7r24nhr3ciy2k6ghernlm

Attention-Enhanced Graph Neural Networks for Session-Based Recommendation

Baocheng Wang, Wentao Cai
2020 Mathematics  
To tackle these defects, we propose a novel model which leverages both the target attentive network and self-attention network to improve the graph-neural-network (GNN)-based recommender.  ...  Session-based recommendation, which aims to match user needs with rich resources based on anonymous sessions, nowadays plays a critical role in various online platforms (e.g., media streaming sites, search  ...  [5] introduced the graph neural network into the session-based recommendation task.  ... 
doi:10.3390/math8091607 doaj:8475fce7998044c4962569669e39f9ec fatcat:mlcvag7jovdzxp3ap5daxkutpm

Graph Contextualized Self-Attention Network for Session-based Recommendation

Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation.  ...  In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN).  ...  Conclusion In this paper, we proposed a graph contextualized selfattention network (GC-SAN) based on graph neural network for session-based recommendation.  ... 
doi:10.24963/ijcai.2019/547 dblp:conf/ijcai/XuZLSXZFZ19 fatcat:ge2hv6gl4ffexokdrhhyzvccw4

Star Graph Neural Networks for Session-based Recommendation

Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, Maarten de Rijke
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
Importantly, GNN-based approaches often face serious overfitting problems. We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation.  ...  Thus graph neural network (GNN) based models have been proposed to capture the transition relationship between items.  ...  ., Star Graph Neural Networks with Highway Networks (SGNN-HN) , for session-based recommendation.  ... 
doi:10.1145/3340531.3412014 dblp:conf/cikm/PanCCCR20 fatcat:gi5jjtxocjhl7l5rzmyibpro34

Knowledge-enhanced Session-based Recommendation with Temporal Transformer [article]

Rongzhi Zhang, Yulong Gu, Xiaoyu Shen, Hui Su
2021 arXiv   pre-print
The item embeddings in a session are passed through the temporal transformer network to get the session embedding, based on which the final recommendation is made.  ...  In this paper, we propose a framework called Knowledge-enhanced Session-based Recommendation with Temporal Transformer (KSTT) to incorporate such information when learning the item and session embeddings  ...  Graph Neural Network for Session-based Recommendation Graph Neural Networks(GNN), which can capture both graph structure and nodes' attributes in the graph, have shown its superiority in many applications  ... 
arXiv:2112.08745v1 fatcat:epftrmha7fdkvm5tg2bvotimna

Nonindependent Session Recommendation Based on Ordinary Differential Equation

Zhenyu Yang, Mingge Zhang, Guojing Liu, Mingyu Li
2020 Mathematical Problems in Engineering  
We used Ordinary Differential Equations to train the Graph Neural Network and could predict forward or backward at any point in time to model the user's nonindependent sessions.  ...  information mining through Deep Neural Networks.  ...  We propose a recommendation model based on Neural ODE: Sess-ODEnet. e model combines differential equations with gated graph neural networks to model complex sessions. e model derives the representation  ... 
doi:10.1155/2020/9290576 fatcat:pduwvozo2fautj5ep57b54krja

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation [article]

Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
2021 arXiv   pre-print
However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics  ...  with the global graph context.  ...  Another paradigm of session-based recommendation models lie in utilizing graph neural networks to capture the graph-structured item dependencies, such as attributed graph neural network for streaming recommendation  ... 
arXiv:2110.03996v1 fatcat:qp5o3osmofgttnnas7r6b6lowu

Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

Baocheng Wang, Wentao Cai
2020 Information  
In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs).  ...  We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge  ...  . ( 7 ) Session-based recommendation with graph neural networks (SR-GNN) [1] generates latent item vectors by using a GNN and attention network for the session-based recommendation task. ( 8 ) KSR  ... 
doi:10.3390/info11080388 fatcat:iueconen5vadrazj64rqcs7w6y

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Xiangde Zhang, Yuan Zhou, Jianping Wang, Xiaojun Lu
2021 Entropy  
Considering the diversity of items and users' interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation.  ...  Session-based recommendations aim to predict a user's next click based on the user's current and historical sessions, which can be applied to shopping websites and APPs.  ...  Conclusions In this paper, we proposed a personalized interest-attention graph neural network, PIA-GNN, based on session recommendation.  ... 
doi:10.3390/e23111500 pmid:34828197 pmcid:PMC8618736 fatcat:ms7keg7bn5am7e7g4nbxmgindy
« Previous Showing results 1 — 15 out of 11,782 results