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Sequential Recommendation with Graph Neural Networks [article]

Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, Yong Li
2021 arXiv   pre-print
In this work, we propose a graph neural network model called SURGE (short for SeqUential Recommendation with Graph neural nEtworks) to address these two issues.  ...  Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation.  ...  There are some works [33] utilizing graph neural networks for session-based recommendation, a problem similar with sequential recommendation.  ... 
arXiv:2106.14226v1 fatcat:c6inmah6qrh2vlsybo6viftwci

Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation [article]

Lyuxin Xue, Deqing Yang, Yanghua Xiao
2022 arXiv   pre-print
Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's  ...  To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences.  ...  Many SR systems were built with sequential models including Markovbased models [1] and recurrent neural network (RNN) based models [2, 3] , where a user's preference is generally represented with his  ... 
arXiv:2207.13262v1 fatcat:cyzjfqi6mfcmpck5m2a7a2y5u4

Sequential Recommendation through Graph Neural Networks and Transformer Encoder with Degree Encoding

Shuli Wang, Xuewen Li, Xiaomeng Kou, Jin Zhang, Shaojie Zheng, Jinlong Wang, Jibing Gong
2021 Algorithms  
In this paper, we propose a novel deep neural network named graph convolutional network transformer recommender (GCNTRec).  ...  constructed under the heterogeneous information networks in an end-to-end fashion through a graph convolutional network (GCN) with degree encoding, while the capturing long-range dependencies of items  ...  GCNTRec empirically investigates the advantage of fusing the transformer encoder model with degree encoding based on a graph neural network for sequential recommendation tasks.  ... 
doi:10.3390/a14090263 fatcat:a2aik27cojejnja6lvh77epqum

Dynamic Graph Neural Networks for Sequential Recommendation [article]

Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
2021 arXiv   pre-print
We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior  ...  We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals into one framework.  ...  What's more, Convolution Neural Network (CNN) is also used in sequential recommendation to investigate the different patterns.  ... 
arXiv:2104.07368v2 fatcat:74kljncl4nfefeehuqjg6g47du

Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

Baocheng Wang, Wentao Cai
2020 Information  
Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data.  ...  In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs).  ...  In this work, we propose a knowledge-enhanced recommendation method using graph neural networks and memory networks to tackle the sequential recommendation task and overcome the limitations mentioned above  ... 
doi:10.3390/info11080388 fatcat:iueconen5vadrazj64rqcs7w6y

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
2022 IEEE Transactions on Knowledge and Data Engineering  
Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications.  ...  Towards this end, we propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns, by exploring the evolving  ...  Sequential/Session-based Recommendation Models with Graph-based Neural Networks: Another important research line of time-aware recommender systems lies in the utilization of graph neural networks for behavior  ... 
doi:10.1109/tkde.2022.3175094 fatcat:iqreqptfvbeeffmit4isv7xsuu

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.  ...  Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.  ...  Then, we introduce the neural networks on graphs. Conventional recommendation methods.  ... 
arXiv:1811.00855v4 fatcat:z7z7pf6skvc7jeocgd2lelekku

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.  ...  Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.  ...  Then, we introduce the neural networks on graphs. Conventional recommendation methods.  ... 
doi:10.1609/aaai.v33i01.3301346 fatcat:x5bhyl5b3jewrbgiocuhvbkh2e

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).  ...  Table 3 : The performance of GC-SAN with and without graph neural network in terms of HR@10 and NDCG@10. Impact of graph neural network.  ... 
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  
We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation.  ...  Previous work on session-based recommendation has considered sequences of items that users have interacted with sequentially.  ...  To address the above issues, we propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation.  ... 
doi:10.1145/3340531.3412014 dblp:conf/cikm/PanCCCR20 fatcat:gi5jjtxocjhl7l5rzmyibpro34

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.  ...  Graph Neural Networks for Recommendation. Recently emerged graph neural networks shine a light on performing information propagation over user-item graph for recommendation.  ... 
arXiv:2110.03996v1 fatcat:qp5o3osmofgttnnas7r6b6lowu

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation [article]

Cheng Hsu, Cheng-Te Li
2021 arXiv   pre-print
We propose a novel deep learning-based model, Relational Temporal Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of RetaGNN is three-fold.  ...  Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones.  ...  In this paper, we propose a novel deep learning-based model, RElational Temporal Attentive Graph Neural Network (RetaGNN), for sequential recommendation.  ... 
arXiv:2101.12457v1 fatcat:i4pdzfjtifeq3g4mxz3b2e3se4

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  ...  Item Encoder with Knowledge-enhanced Graph Neural Network The item encoder uses Knowledge-enhanced Graph Neural Network to learn item embeddings based on the knowledge graph.  ... 
arXiv:2112.08745v1 fatcat:epftrmha7fdkvm5tg2bvotimna

An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Haiyan Wang, Kaiming Yao, Jian Luo, Yi Lin, Honghao Gao
2021 Wireless Communications and Mobile Computing  
In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG).  ...  Secondly, we integrate recurrent neural network and attention mechanism to capture user's evolving interests and relationships between different items in the sequence.  ...  With the rapid development of neural networks, neural networkbased models have been widely used in sequential recommendation tasks, such as recurrent neural network-based models [4] [5] [6] and convolutional  ... 
doi:10.1155/2021/5206228 fatcat:dmn2323kyfh2nppavmyhse4dfe

Inter-sequence Enhanced Framework for Personalized Sequential Recommendation [article]

Feng Liu, Weiwen Liu, Xutao Li, Yunming Ye
2020 arXiv   pre-print
Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph.  ...  Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation.  ...  In particular, inter-sequence item correlation is depicted with graphs. Graph neural networks are used to propagate the information along the paths between any two items.  ... 
arXiv:2004.12118v2 fatcat:aunryocosjhgrfbdjn4a7eoq7m
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