Journal of South China University of Technology(Natural Science) >

2024 , Vol. 52 >Issue 2: 32 - 41

DOI: https://doi.org/10.12141/j.issn.1000-565X.230023

Computer Science & Technology

Social Recommendation Model Based on Dynamic Neighborhood Sampling

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  • School of Information and Communication，Guilin University of Electronic Technology，Guilin 541004，Guangxi，China
蔡晓东（1971-），男，博士，研究员，主要从事大数据挖掘研究。E-mail：caixiaodong@guet.edu.cn

Received date: 2023-01-19

  Online published: 2023-06-20

Supported by

the Guangxi Innovation-Driven Development Special Project(AA20302001)

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Abstract

The social recommendation model based on graph neural network has achieved good performance in improving the performance of the recommendation system. However, the existing methods ignored the possible feature mismatch between the queried target users and content nodes and their neighbors, which leads to the introduction of noise and reduces the model performance. To solve this problem, this paper proposed a social recommendation model DNSSR. Firstly, it constructed a relational graph containing multiple relationships between users and items, with richer information associations between nodes in the graph. Then the dynamic neighborhood sampling mechanism was used to obtain neighbor nodes that are more consistent with the characteristics of the target query pair, reducing noise information. In addition, in order to further improve the predictive performance of the model, this paper designed an enhanced graph neural network to model the sampled relationship subgraphs. It can distinguish the importance of different neighboring nodes and select more reliable information sources to obtain more robust user and item embedding vectors for rating prediction. The experimental results show that the prediction error of this model is significantly reduced compared to that of other advanced models, proving the effectiveness of the methods proposed in the paper. Especially for the dynamic neighborhood sampling mechanism, if it is abandoned, the RMSE and MAE indicators of DNSSR on the Ciao dataset will increase by 6.05% and 7.31% respectively, and the Epinions dataset will increase by 3.49% and 5.41% respectively, which fully demonstrate their effectiveness in reducing noise interference and improving the performance of social recommendation models.

Key words： social recommendation; rating prediction; graph neural network; dynamic neighborhood sampling

Cite this article

CAI Xiaodong, ZHOU Qingsong, YE Qing . Social Recommendation Model Based on Dynamic Neighborhood Sampling[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(2) : 32 -41 . DOI: 10.12141/j.issn.1000-565X.230023

References

1 DA’U A， SALIM N ．Recommendation system based on deep learning methods：asystematic review and new directions［J］．Artificial Intelligence Review，2020，53（4）：2709-2748.
2 JANNACH D， ZANKER M， FELFERNIG A，et al ．Recommender systems：an introduction［M］．Cambridge：Cambridge University Press，2010.
3 MCPHERSON M， SMITH-LOVIN L， COOK J M ．Birds of a feather：homophily in social networks［J］．Annual Review of Sociology，2001，27（1）：415-444.
4 SALAKHUTDINOV R， MNIH A ．Probabilistic matrix factorization［C］∥Proceedings of the 20th International Conference on Neural Information Processing Systems．Vancouver：Curran Associates Inc.，2007：1257-1264.
5 MA H， YANG H， LYU M R，et al ．Sorec：social recommendation using probabilistic matrix factorization［C］∥Proceedings of the 17th ACM Conference on Information and Knowledge Management．California：ACM，2008：931-940.
6 JAMALI M， ESTER M ．A matrix factorization technique with trust propagation for recommendation in social networks［C］∥Proceedings of the Fourth ACM Conference on Recommender Systems．New York：ACM，2010：135-142.
7 YANG B， LEI Y， LIU J，et al ．Social collaborative filtering by trust［J］．IEEE Transactions on Pattern Analysis and Machine Intelligence，2016，39（8）：1633-1647.
8 KIPF T N， WELLING M ．Semi-supervised classification with graph convolutional networks［C］∥Proceedings of the 5th International Conference on Learning Representations．Toulon：ICLR，2017：1-14.
9 WU Z， PAN S， CHEN F，et al ．A comprehensive survey on graph neural networks［J］．IEEE Transactions on Neural Networks and Learning Systems，2020，32（1）：4-24.
10 ZHOU J， CUI G， HU S，et al ．Graph neural networks：a review of methods and applications［J］．AI Open，2020，1：57-81.
11 FAN W， MA Y， LI Q，et al ．Graph neural networks for social recommendation［C］∥Proceedings of the World Wide Web Conference．San Francisco：ACM，2019：417-426.
12 WU Q， ZHANG H， GAO X，et al ．Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems［C］∥Proceedings of the World Wide Web Conference．San Francisco：ACM，2019：2091-2102.
13 XIAO Y， YAO L， PEI Q，et al ．MGNN：mutualistic graph neural network for joint friend and item recommendation［J］．IEEE Intelligent Systems，2020，35（5）：7-17.
14 FAN W， MA Y， LI Q，et al ．A graph neural network framework for social recommendations［J］．IEEE Transactions on Knowledge and Data Engineering，2022，34（5）：2033-2047.
15 ZHANG Y， WU L， SHEN Q，et al ．Graph learning augmented heterogeneous graph neural network for social recommendation［J］．ACM Transactions on Recommender Systems，2023，1（4）：1-22.
16 YU Y H， QIAN W W， ZHANG L，et al ．A graph-neural-network-based social network recommendation algorithm using high-order neighbor information［J］．Sensors，2022，22（19）：7122/1-16.
17 CHEN J， XIN X， LIANG X，et al ．GDSRec：graph-based decentralized collaborative filtering for social recommendation［J］．IEEE Transactions on Knowledge and Data Engineering，2023，35（5）：4813-4824.
18 HUANG C， XU H， XU Y，et al ．Knowledge-aware coupled graph neural network for social recommendation［J］．Proceedings of the AAAI Conference on Artificial Intelligence，2021，35（5）：4115-4122.
19 FU B， ZHANG W， HU G，et al ．Dual side deep context-aware modulation for social recommendation［C］∥Proceedings of the Web Conference 2021．Ljubljana：Association for Computing Machinery，2021：2524-2534.
20 YANG L， LIU Z， DOU Y，et al ．Consisrec：enhancing GNN for social recommendation via consistent neighbor aggregation［C］∥Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval．Montréal：ACM，2021：2141-2145.
21 CHEN T， WONG R C W ．An efficient and effective framework for session-based social recommendation［C］∥Proceedings of the 14th ACM International Conference on Web Search and Data Mining．Israel：ACM，2021：400-408.
22 WANG Z， WEI W， CONG G，et al ．Global context enhanced graph neural networks for session-based recommendation［C］∥Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval．Xi’an：ACM，2020：169-178.
23 FAN W， LI Q， CHENG M ．Deep modeling of social relations for recommendation［C］∥Proceedings of the AAAI Conference on Artificial Intelligence．New Orleans：AAAI，2018，32：8075-8076.
24 KOREN Y ．Factorization meets the neighborhood：amultifaceted collaborative filtering model［C］∥Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining．New York：ACM，2008：426-434.
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