Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (3): 161-168.doi: 10.11896/jsjkx.240500015

• Database & Big Data & Data Science • Previous Articles     Next Articles

Negative Sampling Method for Fusing Knowledge Graph

LU Haiyang1, LIU Xianhui2, HOU Wenlong1   

  1. 1 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    2 CAD Research Center,College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2024-05-06 Revised:2024-08-20 Online:2025-03-15 Published:2025-03-07
  • About author:LU Haiyang,born in 1999,postgra-duate.His main research interests include knowledge graph and recommender systems.
    LIU Xianhui,born in 1979,Ph.D,associate professor.His main research in-terests include machine learning,data mining and big data,and networked manufacturing.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3305700).

PDF (PC)

190

Abstract: In order to solve the problem of information overload,recommender systems have been widely studied.Since it is difficult to obtain a large amount of high-quality explicit feedback data,implicit feedback data becomes the mainstream choice for training re-commender systems.Sampling negative instances from unlabeled data,i.e.negative sampling,is crucial for training recommendation models based on implicit feedback data.The previous negative sampling methods often focus on how to select hard negative instances that contain more user preference information,without considering the false negative problem.In order to reduce the false negative probability of negative instances obtained from sampling and make them more informative,a negative sampling method that integrates knowledge graph is proposed.Firstly,constructing a candidate instance set based on the user-item knowledge graph.Then,the negative instance with the lowest false negative probability is selected from the candidate set through a Bayesian classification approach.Finally,based on the Mixup strategy,positive mixing technology is introduced to construct the hard negative instance.To evaluate the effectiveness of the proposed method,validation was conducted on two public datasets.The results show that compared with previous methods,the method proposed in this paper performs better.

Key words: Negative sampling, Knowledge graph, Recommender system, Positive mixing

CLC Number: 

  • TP391
[1]GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,1992,35(12):61-70.
[2]KABBUR S,NING X,KARYPIS G.Fism:factored item simila-rity models for top-n recommender systems[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:659-667.
[3]KOREN Y.Factorization meets the neighborhood:a multiface-ted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2008:426-434.
[4]HE X,DENG K,WANG X,et al.Lightgcn:Simplifying andpowering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.2020:639-648.
[5]WANG X,HE X,WANG M,et al.Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:165-174.
[6]WANG X,HE X,CAO Y,et al.Kgat:Knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Disco-very & Data Mining.2019:950-958.
[7]GUO X W,XIA H B,LIU Y.Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network[J].Journal of Frontiers of Computer Science and Technology,2022,16(6):1343-1353.
[8]LAI R,CHEN L,ZHAO Y,et al.Disentangled negative sampling for collaborative filtering[C]//Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining.2023:96-104.
[9]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[J].arXiv:1205.2618,2012.
[10]LIU B,WANG B.Bayesian Negative Sampling for Recommendation[C]//2023 IEEE 39th International Conference on Data Engineering.2023:749-761.
[11]ZHANG H,CISSE M,DAUPHIN Y N,et al.mixup:Beyondempirical risk minimization[J].arXiv:1710.09412,2017.
[12]SUN Z,GUO Q,YANG J,et al.Research commentary on recommendations with side information:A survey and research directions[J].Electronic Commerce Research and Applications,2019,37:100879.
[13]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Procee-dings of the 10th International Conference on World Wide Web.2001:285-295.
[14]YOO H,CHUNG K.Deep learning-based evolutionary recommendation model for heterogeneous big data integration[J].Transactions on Internet and Information Systems,2020,14(9):3730-3744.
[15]RAO Z Y,ZHANG Y,LIU J T,et al.Recommendation methods and systems using knowledge graph[J].Acta Automatica Sinica,2021,47(9):2061-2077.
[16]SHI C,ZHANG Z,LUO P,et al.Semantic path based persona-lized recommendation on weighted heterogeneous information networks[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.2015:453-462.
[17]WANG H,ZHANG F,WANG J,et al.Ripplenet:Propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Confe-rence on Information and Knowledge Management.2018:417-426.
[18]MA H D,FANG Y Q.Dynamic Negative Sampling for Graph Convolution Network Based Collaborative Filtering Recommendation Model[J].Computer Science,2023,50(S2):489-495.
[19]DIAZ-AVILES E,DRUMOND L,SCHMIDT-THIEME L,et al.Real-time top-n recommendation in social streams[C]//Proceedings of the Sixth ACM Conference on Recommender Systems.2012:59-66.
[20]CUI P,LIU S,ZHU W.General knowledge embedded imagerepresentation learning[J].IEEE Transactions on Multimedia,2017,20(1):198-207.
[21]HE X,ZHANG H,KAN M Y,et al.Fast matrix factorization for online recommendation with implicit feedback[C]//Procee-dings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:549-558.
[22]TOGASHI R,OTANI M,SATOH S.Alleviating cold-startproblems in recommendation through pseudo-labelling over knowledge graph[C]// Proceedings of the 14th ACM International Conference on Web Search and Data Mining.2021:931-939.
[23]RENDLE S,FREUDENTHALER C.Improving pairwise lear-ning for item recommendation from implicit feedback[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining.2014:273-282.
[24]ZHANG W,CHEN T,WANG J,et al.Optimizing top-n collabo-rative filtering via dynamic negative item sampling[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.2013:785-788.
[25]ZHAO T,MCAULEY J,KING I.Improving latent factor mo-dels via personalized feature projection for one class recommendation[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.2015:821-830.
[26]YU L,ZHOU G,ZHANG C,et al.RankMBPR:Rank-awaremutual bayesian personalized ranking for item recommendation[C]//Web-Age Information Management:17th International Conference.2016:244-256.
[27]ZHAO Y,GUO G B,JIANG L Y.Adversarial Sampling for Social Recommender[J].Journal of Cyber Security,2021,6(5):88-98.
[28]DING J,QUAN Y,YAO Q,et al.Simplify and robustify negative sampling for implicit collaborative filtering[J].Advances in Neural Information Processing Systems,2020,33:1094-1105.
[29]HUANG T,DONG Y,DING M,et al.Mixgcf:An improvedtraining method for graph neural network-based recommender systems[C]// Proceedings of the 27th ACM SIGKDD Confe-rence on Knowledge Discovery & Data Mining.2021:665-674.
[30]WANG J,YU L,ZHANG W,et al.Irgan:A minimax game for unifying generative and discriminative information retrieval models[C]// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.2017:515-524.
[31]PARK D H,CHANG Y.Adversarial sampling and training for semi-supervised information retrieval[C]//The World Wide Web Conference.2019:1443-1453.
[32]ZHAO Y H,LIU L,WANG H L,et al.Survey of Knowledge Graph Recommendation System Research[J].Journal of Frontiers of Computer Science and Technology,2023,17(4):771-791.
[33]ANAGNOSTOPOULOS A,KUMAR R,MAHDIAN M.Influence and correlation in social networks[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2008:7-15.
[34]KALANTIDIS Y,SARIYILDIZ M B,PION N,et al.Hard negative mixing for contrastive learning[J].Advances in Neural Information Processing Systems,2020,33:21798-21809.
[35]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[1] SONG Baoyan, LIU Hangsheng, SHAN Xiaohuan, LI Su, CHEN Ze. Joint Relational Patterns and Analogy Transfer Knowledge Graph Completion Method [J]. Computer Science, 2025, 52(3): 287-294.
[2] WEI Qianqiang, ZHAO Shuliang, ZHANG Siman. Multi-hop Knowledge Base Question Answering Based on Differentiable Knowledge Graph [J]. Computer Science, 2025, 52(3): 295-305.
[3] ZENG Zefan, HU Xingchen, CHENG Qing, SI Yuehang, LIU Zhong. Survey of Research on Knowledge Graph Based on Pre-trained Language Models [J]. Computer Science, 2025, 52(1): 1-33.
[4] CHENG Zhiyu, CHEN Xinglin, WANG Jing, ZHOU Zhongyuan, ZHANG Zhizheng. Retrieval-augmented Generative Intelligence Question Answering Technology Based on Knowledge Graph [J]. Computer Science, 2025, 52(1): 87-93.
[5] LIU Changcheng, SANG Lei, LI Wei, ZHANG Yiwen. Large Language Model Driven Multi-relational Knowledge Graph Completion Method [J]. Computer Science, 2025, 52(1): 94-101.
[6] CHENG Jinfeng, JIANG Zongli. Dialogue Generation Model Integrating Emotional and Commonsense Knowledge [J]. Computer Science, 2025, 52(1): 307-314.
[7] NIU Guanglin, LIN Zhen. Survey of Knowledge Graph Representation Learning for Relation Feature Modeling [J]. Computer Science, 2024, 51(9): 182-195.
[8] CHEN Shanshan, YAO Subin. Study on Recommendation Algorithms Based on Knowledge Graph and Neighbor PerceptionAttention Mechanism [J]. Computer Science, 2024, 51(8): 313-323.
[9] YANG Zhenzhen, WANG Dongtao, YANG Yongpeng, HUA Renyu. Multi-embedding Fusion Based on top-N Recommendation [J]. Computer Science, 2024, 51(7): 140-145.
[10] ZHANG Hui, ZHANG Xiaoxiong, DING Kun, LIU Shanshan. Device Fault Inference and Prediction Method Based on Dynamic Graph Representation [J]. Computer Science, 2024, 51(7): 310-318.
[11] HE Jing, ZHAO Rui, ZHANG Hengshuo. Visual Bibliometric Analysis of Knowledge Graph [J]. Computer Science, 2024, 51(6A): 230500123-10.
[12] TANG Xin, SUN Yufei, WANG Yujue, SHI Min, ZHU Dengming. Three Layer Knowledge Graph Architecture for Industrial Digital Twins [J]. Computer Science, 2024, 51(6A): 230400153-6.
[13] ZHU Yuliang, LIU Juntao, RAO Ziyun, ZHANG Yi, CAO Wanhua. Knowledge Reasoning Model Combining HousE with Attention Mechanism [J]. Computer Science, 2024, 51(6A): 230600209-8.
[14] YIN Baosheng, KONG Weiyi. Electra Based Chinese Event Detection Model with Dependency Syntax Tree [J]. Computer Science, 2024, 51(6A): 230600158-6.
[15] PENG Bo, LI Yaodong, GONG Xianfu, LI Hao. Method for Entity Relation Extraction Based on Heterogeneous Graph Neural Networks and TextSemantic Enhancement [J]. Computer Science, 2024, 51(6A): 230700071-5.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.