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RecStore: Recommending Stores for Shopping Mall Customers

Published: 17 October 2017 Publication History

Abstract

Today, mobility is a key feature in the new generation of Internet, which provides a set of custom services through numerous terminals. Smartphones, for example, are a tendency and almost mandatory to anyone living in an urban and modern context. Most of the developed cities have at least one shopping mall full of mobile devices users. These shopping malls provide a number of stores, and people tend to have difficult in finding what they really need. This paper proposes a solution called RecStore. RecStore is a recommendation model to assist customers in reaching what they consider relevant at malls. The recommendation model considers user activities, 330 stores, 30 users and 3 baseline models. The precision, recall and f-measure improved at rates of 118%, 70% and 88% respectively in comparison to the second best model of each metric. Additionally, a mobile application - called InMap - was implemented based on RecStore.

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Cited By

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  • (2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
  • (2020)An In-store Recommender System Leveraging the Microsoft HoloLensHCI International 2020 - Posters10.1007/978-3-030-50729-9_14(99-107)Online publication date: 10-Jul-2020
  • (2019)How Computer Vision Provides Physical Retail with a Better View on Customers2019 IEEE 21st Conference on Business Informatics (CBI)10.1109/CBI.2019.00060(462-471)Online publication date: Jul-2019
  • Show More Cited By

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Published In

cover image ACM Other conferences
WebMedia '17: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web
October 2017
522 pages
ISBN:9781450350969
DOI:10.1145/3126858
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • SBC: Brazilian Computer Society
  • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
  • CGIBR: Comite Gestor da Internet no Brazil
  • CAPES: Brazilian Higher Education Funding Council

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2017

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Author Tags

  1. customization
  2. mall
  3. mobile
  4. recommendation
  5. shopping

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  • Research-article

Funding Sources

  • Fraunhofer Project Center/UFBA
  • FAPESB - Fundação de Amparo à Pesquisa do Estado da Bahia

Conference

Webmedia '17
Sponsor:
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '17: Brazilian Symposium on Multimedia and the Web
October 17 - 20, 2017
RS, Gramado, Brazil

Acceptance Rates

WebMedia '17 Paper Acceptance Rate 38 of 138 submissions, 28%;
Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

View all
  • (2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
  • (2020)An In-store Recommender System Leveraging the Microsoft HoloLensHCI International 2020 - Posters10.1007/978-3-030-50729-9_14(99-107)Online publication date: 10-Jul-2020
  • (2019)How Computer Vision Provides Physical Retail with a Better View on Customers2019 IEEE 21st Conference on Business Informatics (CBI)10.1109/CBI.2019.00060(462-471)Online publication date: Jul-2019
  • (2018)Filtering Graduate Courses based on LinkedIn ProfilesProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243094(141-147)Online publication date: 16-Oct-2018

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