Mining location-based service data for feature construction in retail store recommendation

Tsung Yi Chen, Lyu Cian Chen, Yuh Min Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In recent years, with the popularization of mobile network, the location- based service (LBS) has made great strides, becoming an efficient marketing instrument for enterprises. For the retail business, good selections of store and appropriate marketing techniques are critical to increasing the profit. However, it is difficult to select the retail store because there are numerous considerations and the analysis was short of metadata in the past. Therefore, this study uses LBS, and provides a recommendation method for retail store selection by analyzing the relationship between the user track and point-of-interest (POI). This study uses regional relevance analysis and human mobility construction to establish the feature values of retail store recommendation. This study proposes (1) architecture of the data model available for retail store recommendation by influential layers of LBS; (2) System-based solution for recommendation of retail stores, adopts the influential factors with specified data in LBS and filtered by industrial types; (3) Industry density, area categories and region/industry clustering methods of POIs. Uses KDE and KMeans to calculate the effect of regional functionality on the retail store selection, similarity is used to calculate the industry category relation, and consumption capacity is considered to state saturation feature.

Original languageEnglish
Title of host publicationAdvances in Data Mining
Subtitle of host publicationApplications and Theoretical Aspects - 17th Industrial Conference, ICDM 2017, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages68-77
Number of pages10
ISBN (Print)9783319627007
DOIs
Publication statusPublished - 2017 Jan 1
Event17th Industrial Conference on Advances in Data Mining, ICDM 2017 - New York, United States
Duration: 2017 Jul 122017 Jul 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10357 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th Industrial Conference on Advances in Data Mining, ICDM 2017
CountryUnited States
CityNew York
Period17-07-1217-07-13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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