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Knowledge-driven Site Selection via Urban Knowledge Graph
[article]
2021
arXiv
pre-print
Site selection determines optimal locations for new stores, which is of crucial importance to business success. Especially, the wide application of artificial intelligence with multi-source urban data makes intelligent site selection promising. However, existing data-driven methods heavily rely on feature engineering, facing the issues of business generalization and complex relationship modeling. To get rid of the dilemma, in this work, we borrow ideas from knowledge graph (KG), and propose a
arXiv:2111.00787v1
fatcat:vkymdi6ytrbhzlr4ub2ypeel5y