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).

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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
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