LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks,ERICDATA高等教育知識庫
高等教育出版
熱門: 謝傳崇  羅文君  葉俊廷  紀金山  SDGs  簡淑芸  
高等教育出版
首頁 臺灣期刊   學校系所   學協會   民間出版   大陸/海外期刊   政府機關   學校系所   學協會   民間出版   DOI註冊服務
篇名
LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks
並列篇名
LighterKGCN: A Recommender System Model based on Bi-layer Graph Convolutional Networks
作者 陳鵬Jiancheng ZhaoXiaosheng Yu
英文摘要

Recommender systems have been extensively utilized to meet users’ personalized needs. Collaborative filtering is one of the most classic algorithms in the recommendation field. However, it has problems such as cold start and data sparsity. In that case, knowledge graphs and graph convolutional networks have been introduced by scholars into recommender systems to solve the above problems. However, the current graph convolutional networks fail to give full play to the advantages of graph convolution since they are employed either in the embedding representations of users and commodity entities, or in the embedding representations between entities of the knowledge graphs. Therefore, LighterKGCN, a recommender system model based on bi-layer graph convolutional networks was proposed in accordance with the KGCN model and the LightGCN model. In the first layer of GCN, the model first learned the embedding representations of users and commodity entities on the user-commodity entity interaction graph. Then, the attained user embedding and commodity embedding were used as the data source for the second layer of GCN. In the second layer, the entity v and its neighborhoods were calculated using the hybrid aggregation function proposed in this paper. The result was taken as the new entity v. According to tests on three public datasets and comparison results with the KGCN, LighterKGCN improved by 0.52% and 51.16% in terms of AUC and F1 performances, respectively on the dataset of MovieLens-20M; LighterKGCN improved by 0.67% and 45.0% in terms of AUC and F1 performances, respectively on the dataset of Yelp2018; and the number was 0.67% and 36.35% in AUC and F1 performances, respectively on the dataset of Last.FM.

 

起訖頁 621-629
關鍵詞 LighterKGCNEmbeddingRecommender systemCollaborative filteringKnowledge graph
刊名 網際網路技術學刊  
期數 202205 (23:3期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022052303020  複製DOI
QR Code
該期刊
上一篇
Medicine Safety Assessment Method based on Dynamic Dual Optimization
該期刊
下一篇
An Empirical Study of Gradient-based Explainability Techniques for Self-admitted Technical Debt Detection

高等教育知識庫  閱讀計畫  教育研究月刊  新書優惠  

教師服務
合作出版
期刊徵稿
聯絡高教
高教FB
讀者服務
圖書目錄
教育期刊
訂購服務
活動訊息
數位服務
高等教育知識庫
國際資料庫收錄
投審稿系統
DOI註冊
線上購買
高點網路書店 
元照網路書店
博客來網路書店
教育資源
教育網站
國際教育網站
關於高教
高教簡介
出版授權
合作單位
知識達 知識達 知識達 知識達 知識達 知識達
版權所有‧轉載必究 Copyright2011 高等教育文化事業有限公司  All Rights Reserved
服務信箱:edubook@edubook.com.tw 台北市館前路 26 號 6 樓 Tel:+886-2-23885899 Fax:+886-2-23892500