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Social Event Recommendation Based on Social Relationship and Attention Mechanism

Published: 31 August 2021 Publication History

Abstract

Event recommendation based on social relationship is a common method in EBSNs. This kind of algorithm can solve the problem of cold start and data sparsity to a certain extent, but it doesn‘’t consider the influence degree of diffrent friends and the influence degree of diffrent groups on events. In this paper, a novel method for social event recommendation based on social relationship and self-attention (AtSoRec) was developed. The algorithm gets the trust weights among different friends and users by the attention model training. In the process of training, the information of groups and events is integrated into the module. Then, the final Top-k recommendation is obtained by Matrix factorization algorithm. Through the experiments on meetup and plancast data set, it is proved that the algorithm has a good performance on solving the problem of cold start.

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Cited By

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  • (2023)MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social NetworksElectronics10.3390/electronics1301011213:1(112)Online publication date: 27-Dec-2023
  • (2022)Migrating social event recommendation over microblogsProceedings of the VLDB Endowment10.14778/3551793.355186415:11(3213-3225)Online publication date: 1-Jul-2022

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        cover image ACM Other conferences
        ICMAI '21: Proceedings of the 2021 6th International Conference on Mathematics and Artificial Intelligence
        March 2021
        142 pages
        ISBN:9781450389464
        DOI:10.1145/3460569
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 31 August 2021

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        Author Tags

        1. Matrix factorization
        2. cold start
        3. self-Attention
        4. social networks

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        Funding Sources

        • Heilongjiang Postdoctoral Science Foundation Funded Project
        • the National Natural Science Foundation of China
        • Research Program of Heilongjiang Agricultural Reclamation Bureau
        • the National Natural Science and Technology Major Projects
        • Heilongjiang Bayi Agricultural University Support Program for San Heng San Zong
        • the Liaoning Province Science and Technology Projects
        • Promotion project of Heilongjiang Agricultural Reclamation Bureau
        • the Fundamental Research Funds for the Central Universities
        • Heilongjiang Bayi Agricultural University Research Startup Project

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        View all
        • (2023)MFM: A Multiple-Features Model for Leisure Event Recommendation in Geotagged Social NetworksElectronics10.3390/electronics1301011213:1(112)Online publication date: 27-Dec-2023
        • (2022)Migrating social event recommendation over microblogsProceedings of the VLDB Endowment10.14778/3551793.355186415:11(3213-3225)Online publication date: 1-Jul-2022

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