文献类型：期刊文献

英文题名：MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social Networks

作者：Wu, Yazhao[1];Peng, Xia[2,3,4];Niu, Yueyan[5];Gui, Zhiming[1]

第一作者：Wu, Yazhao

通讯作者：Peng, X[1];Peng, X[2];Peng, X[3]

机构：[1]Beijing Univ Technol, Fac Informat, Beijing 100124, Peoples R China;[2]Beijing Union Univ, Tourism Coll, Beijing 100101, Peoples R China;[3]Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;[4]Beijing Key Lab Urban Spatial Informat Engn, Beijing 100045, Peoples R China;[5]Beijing Union Univ, Coll Appl Arts & Sci, Beijing 100191, Peoples R China

第一机构：Beijing Univ Technol, Fac Informat, Beijing 100124, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Tourism Coll, Beijing 100101, Peoples R China;[2]corresponding author), Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;[3]corresponding author), Beijing Key Lab Urban Spatial Informat Engn, Beijing 100045, Peoples R China.|[1141732]北京联合大学旅游学院;[11417]北京联合大学;

年份：2024

卷号：13

期号：1

外文期刊名：ELECTRONICS

收录：;Scopus(收录号：2-s2.0-85181962604);WOS:【SCI-EXPANDED(收录号:WOS:001139302300001)】；

基金：No Statement Available

语种：英文

外文关键词：event recommendation system; EBSN; cold start; user activity; social relations

摘要：Event-based social networks (EBSNs) are rich in information about users and leisure events. The willingness of users to participate in leisure events is influenced by many factors such as event time, location, content, organizer, and social relationship factors of users. Event recommendation systems in EBSNs can help leisure event organizers to accurately find users who want to participate in events. However, to address the existing cold-start problems and improve the accuracy of event recommendations, we propose a multiple-feature-based leisure event recommendation model (MFM). We introduce the user's social contacts into the user preference features and construct a user feature space by integrating the features of the user preferences for events and organizers and preferences of the user's closest friends. Moreover, considering the behavioral differences between active and inactive users, we extracted the respective features and trained the feature weight models. Finally, the experimental results showed that in comparison with the baseline models, the precision of the MFM is higher by at least 7.9%.