A social route recommender mechanism for store shopping support

Yung-Ming Li*, Lien Fa Lin, Chun Chih Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

To survive in a fiercely competitive business environment, it has become increasingly important for physical retailers to provide customers with services offering a better shopping experience. Many renovate and enlarge their shopping spaces to make their stores more enjoyable places to visit. The growth in social media and the use of mobile devices provide retailers with an opportunity to offer a context-aware guidance service to enhance customers’ in-store shopping experience. In this research, by extracting and analysing shopping information (shopping context, visiting trajectory) and social information (user's interest, friends’ influence), a contextual store shopping recommendation system is proposed to provide an appropriate route for first-time customers or those who are unfamiliar with a retailer's shopping space. Our experimental results show that the proposed model is effective in providing an appropriate shopping route and enhancing users’ shopping experience, which could significantly improve the profitability and competitive advantage of the retailers.

Original languageEnglish
Pages (from-to)97-108
Number of pages12
JournalDecision Support Systems
Volume94
DOIs
StatePublished - 1 Feb 2017

Keywords

  • Markov chain
  • Shopping context
  • Social network analysis
  • User preference

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