Resident-Based Store Recommendation Model for Community Commercial Planning

Kaiwen Wu; Yanhu Li; Xiaofeng He

doi:10.1007/978-3-031-46661-8_54

Resident-Based Store Recommendation Model for Community Commercial Planning

Kaiwen Wu, Yanhu Li, Xiaofeng He

School of Computer Science and Technology

East China Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings
Editors	Xiaochun Yang, Bin Wang, Heru Suhartanto, Guoren Wang, Jing Jiang, Bing Li, Huaijie Zhu, Ningning Cui
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	818-830
Number of pages	13
ISBN (Print)	9783031466601
DOIs	https://doi.org/10.1007/978-3-031-46661-8_54
State	Published - 2023
Event	19th International Conference on Advanced Data Mining and Applications, ADMA 2023 - Shenyang, China Duration: 21 Aug 2023 → 23 Aug 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14176 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Advanced Data Mining and Applications, ADMA 2023
Country/Territory	China
City	Shenyang
Period	21/08/23 → 23/08/23

Keywords

Community commercial planning
Encoder-decoder
Store recommendation

Access to Document

10.1007/978-3-031-46661-8_54

Cite this

Wu, K., Li, Y., & He, X. (2023). Resident-Based Store Recommendation Model for Community Commercial Planning. In X. Yang, B. Wang, H. Suhartanto, G. Wang, J. Jiang, B. Li, H. Zhu, & N. Cui (Eds.), Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings (pp. 818-830). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14176 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-46661-8_54

Wu, Kaiwen ; Li, Yanhu ; He, Xiaofeng. / Resident-Based Store Recommendation Model for Community Commercial Planning. Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. editor / Xiaochun Yang ; Bin Wang ; Heru Suhartanto ; Guoren Wang ; Jing Jiang ; Bing Li ; Huaijie Zhu ; Ningning Cui. Springer Science and Business Media Deutschland GmbH, 2023. pp. 818-830 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Resident-Based Store Recommendation Model for Community Commercial Planning",

abstract = "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.",

keywords = "Community commercial planning, Encoder-decoder, Store recommendation",

author = "Kaiwen Wu and Yanhu Li and Xiaofeng He",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 19th International Conference on Advanced Data Mining and Applications, ADMA 2023 ; Conference date: 21-08-2023 Through 23-08-2023",

year = "2023",

doi = "10.1007/978-3-031-46661-8\_54",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Wu, K, Li, Y & He, X 2023, Resident-Based Store Recommendation Model for Community Commercial Planning. in X Yang, B Wang, H Suhartanto, G Wang, J Jiang, B Li, H Zhu & N Cui (eds), Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14176 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 818-830, 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, Shenyang, China, 21/08/23. https://doi.org/10.1007/978-3-031-46661-8_54

Resident-Based Store Recommendation Model for Community Commercial Planning. / Wu, Kaiwen; Li, Yanhu; He, Xiaofeng.
Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. ed. / Xiaochun Yang; Bin Wang; Heru Suhartanto; Guoren Wang; Jing Jiang; Bing Li; Huaijie Zhu; Ningning Cui. Springer Science and Business Media Deutschland GmbH, 2023. p. 818-830 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14176 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - He, Xiaofeng

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

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KW - Store recommendation

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BT - Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 19th International Conference on Advanced Data Mining and Applications, ADMA 2023

Y2 - 21 August 2023 through 23 August 2023

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Wu K, Li Y, He X. Resident-Based Store Recommendation Model for Community Commercial Planning. In Yang X, Wang B, Suhartanto H, Wang G, Jiang J, Li B, Zhu H, Cui N, editors, Advanced Data Mining and Applications - 19th International Conference, ADMA 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 818-830. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-46661-8_54