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When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation [article]

Yu Tian and Jianxin Chang and Yannan Niu and Yang Song and Chenliang Li
2022 arXiv   pre-print
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history.  ...  To this end, in this paper, we propose a unified multi-grained neural model(named MGNM) via a combination of multi-interest learning and graph convolutional aggregation.  ...  Specifically, MGNM is developed with two major components: user-aware graph convolution and sequential capsule network.  ... 
arXiv:2205.01286v1 fatcat:wlct2okdyrfbtjvllpb6f5fhmi

A Graph Convolution Network Based on Improved Density Clustering for Recommendation System

Yue Li
2022 Information Technology and Control  
convolution neural network to improve the recommendation effect.  ...  Finally, the features of clustersubgraphs are processed by global graph convolution network and the recommendation results are generatedaccording to the global graph features.  ...  Compared with the traditional convolution network, the graph convolution network has the same properties.  ... 
doi:10.5755/j01.itc.51.1.28720 fatcat:u576zhifibfktp7f6v4fwrhtr4

Graph Meta Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo
2021 arXiv   pre-print
To tackle the above challenges, we propose a Multi-Behavior recommendation framework with Graph Meta Network to incorporate the multi-behavior pattern modeling into a meta-learning paradigm.  ...  In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple behavior types (e.g., click, tag-as-favorite, purchase).  ...  Hence, to address the above challenges, in this paper, we propose a new Multi-Behavior recommendation framework with Graph Meta Network (MB-GMN).  ... 
arXiv:2110.03969v1 fatcat:w4mrfiyvabgsvhcu7jzototitu

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information.  ...  Figure 2 : 2 Graph neural network for social recommendation with relation aggregation under message passing paradigm. by the prevalence of spectral graph learning framework-Graph Convolutional Network  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
2022 IEEE Transactions on Knowledge and Data Engineering  
In this work, we tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns.  ...  In many real-life online platforms, user-item interactive behaviors are often multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type behavior inter-dependencies.  ...  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

A review of recommendation system research based on bipartite graph

Ziteng Wu, Chengyun Song, Yunqing Chen, Lingxuan Li, I. Barukčić
2021 MATEC Web of Conferences  
An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop  ...  The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect  ...  in the neighborhood of the first-order graph, spread the information of the distant multi-level neighborhood through the stacked multi-layer graph convolutional network, and finally update the embedding  ... 
doi:10.1051/matecconf/202133605010 fatcat:qyiiqjnev5eublqlmps4gcworq

Graph Heterogeneous Multi-Relational Recommendation

Chong Chen, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu, Shaoping Ma
2021 AAAI Conference on Artificial Intelligence  
To explore the high-hop heterogeneous user-item interactions, we take the advantages of Graph Convolutional Network (GCN) and further improve it to jointly embed both representations of nodes (users and  ...  In this work, we propose a new multi-relational recommendation model named Graph Heterogeneous Collaborative Filtering (GHCF).  ...  In this paper, we present a graph heterogeneous collaborative filtering model, which incorporates heterogeneous feedback data in graph convolutional networks for recommendation with multiple user behaviors  ... 
dblp:conf/aaai/ChenMZWHW0M21 fatcat:qub5ddkglzh4lomoxhl5icj2pa

A Behavior-aware Graph Convolution Network Model for Video Recommendation [article]

Wei Zhuo, Kunchi Liu, Taofeng Xue, Beihong Jin, Beibei Li, Xinzhou Dong, He Chen, Wenhai Pan, Xuejian Zhang, Shuo Zhou
2021 arXiv   pre-print
Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos.  ...  Despite lots of existing recommendation methods, user behaviors on videos, which imply the complex relations between users and videos, are still far from being fully explored.  ...  To fully understand user preferences on videos, we quantify the behaviors as weights on the edges while building the bipartite graph, and then design a graph convolution network (GCN) to propagate the  ... 
arXiv:2106.15402v1 fatcat:w7dc3i2mfjd33brk3cigxrcmsi

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).  ...  on the graphs 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  ...  For graph data in non-Euclidean space, the ACK [13] algorithm based on attention mechanism, combined with the graph convolution neural network, is also applied to the recommendation algorithm of a large-scale  ... 
doi:10.3390/a14090263 fatcat:a2aik27cojejnja6lvh77epqum

Multi-channel Convolutional Neural Network Feature Extraction for Session Based Recommendation

Zhenyan Ji, Mengdan Wu, Yumin Feng, José Enrique Armendáriz Íñigo, Dan Selisteanu
2021 Complexity  
In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi-channel Convolutional Neural Network for session-based recommendation, RMMCNN for brevity.  ...  Specifically, we capture items' internal features from three dimensions through multi-channel convolutional neural network firstly.  ...  Convolutional Neural Network (CNN). Cai et al. [24] propose a multi-domain recommendation method based on CNN.  ... 
doi:10.1155/2021/6661901 fatcat:5m6hln2znvfsfh5holnny5kbvy

Group-Buying Recommendation for Social E-Commerce [article]

Jun Zhang, Chen Gao, Depeng Jin, Yong Li
2020 arXiv   pre-print
We then develop a graph convolutional network model with multi-view embedding propagation, which can extract the complicated high-order graph structure to learn the embeddings.  ...  In this work, we take the first step to approach the problem of group-buying recommendation for social e-commerce and develop a GBGCN method (short for Group-Buying Graph Convolutional Network).  ...  Graph Convolutional Networks for Recommendation Graph convolutional network [56] has become a new stateof-the-art approach of graph representation learning.  ... 
arXiv:2010.06848v2 fatcat:ewdu4gbz7zed3pwfzd3mybqwee

Contrastive Meta Learning with Behavior Multiplicity for Recommendation [article]

Wei Wei and Chao Huang and Lianghao Xia and Yong Xu and Jiashu Zhao and Dawei Yin
2022 arXiv   pre-print
target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling.  ...  In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.  ...  In particular: i) In the first stage, we integrate the behavior-aware graph neural network (with cloned state) and contrastive meta network, to learn initial parameter space of our multi-behavior contrastive  ... 
arXiv:2202.08523v1 fatcat:mvx3u5hxkvh2hekxisptmupyp4

Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations

Liwei Huang, Yutao Ma, Yanbo Liu, Bohong Danny Du, Shuliang Wang, Deyi Li
2022 ACM Transactions on Information Systems  
Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions.  ...  of users and items simultaneously on the bipartite graph with a self-attention aggregator.  ...  [34] then proposed a graph multi-scale pyramid network with convolutional-recurrent encoders to extract the categorical-temporal pattern of user behavior at each time scale.  ... 
doi:10.1145/3511700 fatcat:5jbvfmzbqng7lcegj5eeqk33uu

Light Graph Convolutional Collaborative Filtering with Multi-aspect Information

Denghua Mei, Niu Huang, Xin Li
2021 IEEE Access  
INDEX TERMS Recommender systems, graph convolutional network, representation learning, multi-aspect information.  ...  Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering.  ...  Then feed these graphs into an elaborately designed multi-component model equipped with light graph convolutional network to learn different aspects and different hop embeddings of user and item.  ... 
doi:10.1109/access.2021.3061915 fatcat:yeus775p5nebvcrpjbzqse7f7q

Behavior analysis in social networks: Challenges, technologies, and trends

Meng Wang, Ee-Peng Lim, Lei Li, Mehmet Orgun
2016 Neurocomputing  
The second paper, "Effective Successive POI Recommendation Inferred with Individual Behavior and Group", introduces a point-of-interest recommendation method that combines the factors of successive behaviors  ...  The sixth paper "Pornographic Image Detection Utilizing Deep Convolutional Neural Networks" introduces a pornographic image detection using a convolutional neural network.  ... 
doi:10.1016/j.neucom.2016.06.008 fatcat:x5mumxc3orduxewwvwsdxdar54
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