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Intell. Syst. Technol."],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>\n            Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users\u2019 preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1)\n            <jats:italic>data scarcity in new cities,<\/jats:italic>\n            so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2)\n            <jats:italic>data distribution discrepancy among different cities,<\/jats:italic>\n            so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.\n          <\/jats:p>","DOI":"10.1145\/3447271","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T15:45:02Z","timestamp":1619019902000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer"],"prefix":"10.1145","volume":"12","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7631-3386","authenticated-orcid":false,"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, China"}]},{"given":"Daqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"T\u00e9l\u00e9com SudParis, France"}]},{"given":"Djamal","family":"Zeghlache","sequence":"additional","affiliation":[{"name":"T\u00e9l\u00e9com SudParis, France"}]},{"given":"Jingmin","family":"Chen","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Sizhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Dan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Xinlei","family":"Shi","sequence":"additional","affiliation":[{"name":"Alibaba Group"}]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, China"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"87","article-title":"Location decision making: The case of retail service development in a closed population","volume":"15","author":"Athiyaman Adee","year":"2011","unstructured":"Adee Athiyaman . 2011 . Location decision making: The case of retail service development in a closed population . Acad. Market. Stud. J. 15 , 1 (2011), 87 . Adee Athiyaman. 2011. Location decision making: The case of retail service development in a closed population. Acad. Market. Stud. J. 15, 1 (2011), 87.","journal-title":"Acad. Market. Stud. J."},{"key":"e_1_2_1_2_1","article-title":"Retail revenue management: Applying data-driven analytics to the merchandise line of business","volume":"5","author":"Bata Seth A.","year":"2011","unstructured":"Seth A. Bata , Jonathan Beard , Erica Egri , and David Morris . 2011 . Retail revenue management: Applying data-driven analytics to the merchandise line of business . J. Bus. Retail Manag. Rese. 5 , 2 (2011). Seth A. Bata, Jonathan Beard, Erica Egri, and David Morris. 2011. Retail revenue management: Applying data-driven analytics to the merchandise line of business. J. Bus. Retail Manag. Rese. 5, 2 (2011).","journal-title":"J. Bus. Retail Manag. 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