Collaborative Filtering Model of Graph Neural Network Based on Random Walk
Source abstract
Abstract
This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Advanced Graph Neural Networks · Expert finding and Q&A systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121000, China
Funding
Innovation Project of Postgraduate Education Reform of Liaoning University of Technology · Liaoning Natural Science Foundation Mentoring Program Project · Liaoning Education Department Scientific Research Project · Liaoning Higher Education Innovation Talent Support Project
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