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Resident-Based Store Recommendation Model for Community Commercial Planning

  • Kaiwen Wu
  • , Yanhu Li
  • , Xiaofeng He*
  • *此作品的通讯作者
  • East China Normal University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The objective of community commercial planning is to identify appropriate stores to operate in a community shopping center, catering to the daily needs of residents and enhancing the appeal of the shopping center. However, obtaining data on the characteristics of all residents in the community is a major challenge, and practical methods for selecting suitable stores based on resident characteristics are unavailable. To address these issues, we propose a model that leverages mutual information maximization to learn representations of valuable residents in the shopping area and assess their value. Our key innovation is a value-ranking encoder-decoder that learns the characteristics of all residents in the community and recommends the most suitable store for each storefront. To balance the diversity and competition of businesses within the shopping center, we introduce a diversity loss function. Extensive experimental results show the effectiveness of our model.

源语言英语
主期刊名Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
编辑Xiaochun Yang, Bin Wang, Heru Suhartanto, Guoren Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
出版商Springer Science and Business Media Deutschland GmbH
818-830
页数13
ISBN(印刷版)9783031466601
DOI
出版状态已出版 - 2023
活动19th International Conference on Advanced Data Mining and Applications, ADMA 2023 - Shenyang, 中国
期限: 21 8月 202323 8月 2023

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14176 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议19th International Conference on Advanced Data Mining and Applications, ADMA 2023
国家/地区中国
Shenyang
时期21/08/2323/08/23

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