Semi-Supervised Regression for Evaluating Convenience Store Location
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1389-1393.
Abstract
Location plays a very important role in the retail business due to its huge and long-term investment. In this paper, we propose a novel semi-supervised regression model for evaluating convenience store location based on spatial data analysis. First, the input features for each convenience store can be extracted by analyzing the elements around it based on a geographic information system, and the turnover is used to evaluate its performance. Second, considering the practical application scenario, a manifold regularization model with one semi-supervised performance information constraint is provided. The promising experimental results in the real-world dataset demonstrate the effectiveness of the proposed approach in performance prediction of certain candidate locations for new convenience store opening.Xinxin Bai, Gang Chen, Qiming Tian, Wenjun Yin, Jin Dong
@inproceedings{ijcai2009p233,
title = {Semi-Supervised Regression for Evaluating Convenience Store Location},
author = {Bai, Xinxin and Chen, Gang and Tian, Qiming and Yin, Wen Jun and Dong, Jin},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1389--1393},
year = {2009},
url = {https://ijcai.org/proceedings/2009/233},
}
TY - CPAPER TI - Semi-Supervised Regression for Evaluating Convenience Store Location AU - Bai, Xinxin AU - Chen, Gang AU - Tian, Qiming AU - Yin, Wen Jun AU - Dong, Jin T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1389 EP - 1393 UR - https://ijcai.org/proceedings/2009/233 ER -Copied
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