文献类型：期刊文献

英文题名：Dynamic graph attention network for local leisure event recommendation in event-based social networks

作者：Peng, Xia[1];Wu, Yazhao[2];Gui, Zhiming[2];Liu, Yitian[3];Huang, Zhou[3];Bao, Yi[3]

第一作者：彭霞

通讯作者：Bao, Y[1]

机构：[1]Beijing Union Univ, Tourism Coll, Beijing, Peoples R China;[2]Beijing Univ Technol, Fac Informat, Beijing, Peoples R China;[3]Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China

第一机构：北京联合大学旅游学院

通讯机构：[1]corresponding author), Yiheyuan Rd 5, Beijing 100871, Peoples R China.

年份：2025

卷号：18

期号：1

外文期刊名：INTERNATIONAL JOURNAL OF DIGITAL EARTH

收录：;EI(收录号：20252918811147);Scopus(收录号：2-s2.0-105010841092);WOS:【SCI-EXPANDED(收录号:WOS:001530445600001)】；

基金：We acknowledge the financial support from the National Natural Science Foundation of China (U2344216, 42271471, 42401559) and National Postdoctoral Program for Innovative Talents (BX20240001).

语种：英文

外文关键词：Event-based social networks; event recommendation; graph attention network; dynamic social network; cold-start problem

摘要：Local leisure events satisfy personalized needs and boost local tourism vitality. However, research on leisure event preference recommendations remains inadequate. Event-Based Social Networks (EBSNs) combine online social and offline interactions, but users face information overload, requiring efficient recommendation systems. Current systems face two key challenges: cold-start problems for new users/events and data sparsity. Research shows social relationships help mitigate cold-start issues, while users' interests and social connections change over time, with recent behaviors being more predictive than long-term ones-a fact often overlooked. To address these issues, we propose ERDGAT, a dynamic graph attention network model for event recommendations in EBSNs. The model extracts event features, mines user preferences from historical events, models social relationships using graph attention networks, and captures recent preference features through temporal social networks with long short-term memory networks. Experiments on the Douban Events dataset demonstrate ERDGAT significantly outperforms baseline methods in recommendation accuracy and cold-start mitigation, improving NDCG@10 by 26.5%.