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Weighted Forwarding in graph convolution networks for Recommendation Information Systems

Computers, Materials & Continua 2024年第2期78卷 1897-1914页

作者： Sang-min Lee Namgi Kim Department of Computer Science Kyonggi UniversitySuwonKorea

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the *** convolution network(GCN)algorithms have been employed to implement the RIS ***,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning *** address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)*** proposed method involves multiplying the embedding results with different weights for each hop layer during graph *** applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding *** approach facilitates the learning of more hop layers within the GCN *** efficacy of the WF-GCN was demonstrated through its application to various *** the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,***,in the *** dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,***,the application of WF-GCN to Self-supervised graph learning(SGL)and Simple graph Contrastive learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

关键词： Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems

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