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Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph

Donglei Lu, Dongjie Zhu, Haiwen Du, Yundong Sun, Yansong Wang, Xiaofang Li, Rongning Qu, Ning Cao, Russell Higgs
2022 Computer systems science and engineering  
Furthermore, the combination of the recommended algorithm based on collaborative filtration and other auxiliary knowledge base is an effective way to improve the performance of the recommended system,  ...  CoFM, a fusion recommendation model combining the collaborative filtering model FM and the graph embedding model TransE, introduces the information of many entities and their relations in the knowledge  ...  the FM based on collaborative filtering and the TransE model based on graph embedding.  ... 
doi:10.32604/csse.2022.021525 fatcat:oubexottsbhxtpqnd6mykjxscm

Fusion Knowledge Graph and Collaborative Filtering Recommendation Algorithm

2020 International Journal of Advanced Trends in Computer Science and Engineering  
Therefore, this paper proposes a collaborative filtering algorithm that fuses knowledge graph.  ...  The influence of data sparse, the collaborative filtering recommendation algorithm has the problem of inaccurate recommendation.  ...  To effectively recommend, based on the basic idea of item collaborative filtering recommendation and knowledge graph, this paper proposes a collaborative filtering algorithm that fuses knowledge graph.  ... 
doi:10.30534/ijatcse/2020/268952020 fatcat:nsspxzparvabxp3imvhc5jktbi

Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph

Ruihui Mu, Xiaoqin Zeng
2018 Mathematical Problems in Engineering  
algorithm based on knowledge graph.  ...  Experimental results show that the proposed algorithm can get higher values on precision, recall, and F-measure for collaborative filtering recommendation.  ...  From Figures 11-13 , it can be seen that the collaborative filtering recommendation algorithm based on knowledge graph is superior to the other collaborative filtering recommendation algorithm.  ... 
doi:10.1155/2018/9617410 fatcat:lqtanvo2y5hvrmkrb5hy62pwie

DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network

Yan Xiao, Congdong Li, Vincenzo Liu
2022 Mathematics  
Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation  ...  In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed.  ...  Traditional recommendation systems include recommendation algorithms based on collaborative filtering [6, 7] (user-based collaborative filtering, content-based collaborative filtering) and hybrid recommendation  ... 
doi:10.3390/math10050721 fatcat:z3jylmbkgzfrziwf3zc5fcluci

A Group Recommendation System of Network Document Resource Based on Knowledge Graph and LSTM in Edge Computing

Yuezhong Wu, Qiang Liu, Rongrong Chen, Changyun Li, Ziran Peng, Xiaolong Xu
2020 Security and Communication Networks  
The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing  ...  This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and  ...  For processing data through LSTM in edge computing, the proposed system combines group recommendation, collaborative filtering-based recommendation, and content-based recommendation based on knowledge.  ... 
doi:10.1155/2020/8843803 fatcat:eeezr7ijhbbs3abj36d2th7z44

An Efficient Knowledge-Graph-Based Web Service Recommendation Algorithm

Zhiying Cao, Xinghao Qiao, Shuo Jiang, Xiuguo Zhang
2019 Symmetry  
In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed.  ...  in a Web service knowledge graph.  ...  ., and X.Z. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym11030392 fatcat:ka6h54duizclzh2yviqawyctnm

Transformer-Empowered Content-Aware Collaborative Filtering [article]

Weizhe Lin, Linjun Shou, Ming Gong, Pei Jian, Zhilin Wang, Bill Byrne, Daxin Jiang
2022 arXiv   pre-print
of knowledge-graph-based collaborative filtering systems to exploit item content information.  ...  Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information  ...  Recommender systems based on Transformers for Content-based Filtering have different properties from KG-based Collaborative Filtering systems, and fusing the two approaches is not yet addressed in the  ... 
arXiv:2204.00849v1 fatcat:haphrtxrezah7kogkgupceik6q

Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering

Gongwen Xu, Guangyu Jia, Lin Shi, Zhijun Zhang, Ahmed Mostafa Khalil
2021 Computational Intelligence and Neuroscience  
In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed.  ...  items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm.  ...  Acknowledgments is work was supported in part by the Shandong Education Department Teaching Reform Project (Z2016M014, Z2016M016, and Z2016Z013).  ... 
doi:10.1155/2021/9590502 pmid:34616447 pmcid:PMC8487836 fatcat:6hp6ghyzvzh4xkhs4aissqwcxa

Augmenting Collaborative Recommenders by Fusing Social Relationships: Membership and Friendship [chapter]

Quan Yuan, Li Chen, Shiwan Zhao
2012 Intelligent Systems Reference Library  
In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender methods in order to more  ...  Indepth analysis on the experimental data particularly shows the significant improvement by up to 8% on recommendation accuracy, by embedding social relationships in CF via graph model.  ...  To the best of our knowledge, the work is one of the first attempts to explore the effect of membership in addition to friendship, and to fuse both of them based on random walk graph model with collaborative  ... 
doi:10.1007/978-3-642-25694-3_8 fatcat:qkjm2bhiknho7beojzkia7wg4y

Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation

Meng Jian, Chenlin Zhang, Xin Fu, Lifang Wu, Zhangquan Wang
2022 Sensors  
Collaborative filtering-based models perform recommendation relying on users' historical interactions, which meets great difficulty in modeling users' interests with extremely sparse interactions.  ...  Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload.  ...  Related Work The proposed KMEL model is related to collaborative filtering, graph-based recommendation, and knowledge-aware recommendation models.  ... 
doi:10.3390/s22062212 pmid:35336383 pmcid:PMC8954710 fatcat:zsuwtkqmwbc5zgm7ijvhbrkjk4

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.  ...  Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload.  ...  Collaborative Filtering Based Recommendation Collaborative filtering has emerged as the most popular paradigm to build recommender systems.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

Movie Recommendation System Mistreatment Current Trends and Sentiment Analysis from Micro Blogging Knowledge

J Manikandan
2021 International Journal for Research in Applied Science and Engineering Technology  
Keywords: Collaborative filtering, Content based filtering, Recommendation System, Sentiment Analysis, Twitter  ...  Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user  ...  Collaborative Filtering To address some of the limitations of content-based filtering, collaborative filtering uses similarities between users and items simultaneously to provide recommendations.  ... 
doi:10.22214/ijraset.2021.38651 fatcat:g5znwqm72ffkfalksofctal5va

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [article]

Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo
2021 arXiv   pre-print
Accurate user and item embedding learning is crucial for modern recommender systems.  ...  To tackle these challenges, this work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT), to investigate multi-typed interactive patterns between users and items in recommender  ...  Acknowledgments We thank the anonymous reviewers for their constructive feedback and comments.  ... 
arXiv:2110.04000v1 fatcat:44xhyegydzbmzlf5ytlznzhrqm

Light Graph Convolutional Collaborative Filtering with Multi-aspect Information

Denghua Mei, Niu Huang, Xin Li
2021 IEEE Access  
Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering.  ...  INDEX TERMS Recommender systems, graph convolutional network, representation learning, multi-aspect information.  ...  RELATED WORK In this section, we briefly introduce existing work on Collaborative Filtering and GCN-based method for recommendation, which are most relevant to our study. A.  ... 
doi:10.1109/access.2021.3061915 fatcat:yeus775p5nebvcrpjbzqse7f7q

Recommendation method for fusion of knowledge graph convolutional network

Xiaolin Jiang, Yu Fu, Changchun Dong
2022 EURASIP Journal on Advances in Signal Processing  
This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph,  ...  Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.  ...  Acknowledgements The authors acknowledged the anonymous reviewers and editors for their efforts in valuable comments and suggestions.  ... 
doi:10.1186/s13634-022-00854-7 fatcat:gg54q6ekdjhrtmzs3t4fd43lxq
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