Combining social media and location-based services for shop type recommendation

Miao Tian, Bin Guo, Zhiwen Yu, Zhu Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

It is an important yet challenging task for investors to determine the most suitable type of shop (e.g., restaurant, fashion, etc.) for a newly opened store. In this paper, we present a shop type recommendation system, which can be used for multiple parties to make investment decisions. We adopt two types of features, location features and commercial features to model a shop, which are derived from the heterogeneous data, i.e., social media and location-based services (LBS). A novel bias learning matrix factorization method with feature fusion is proposed for recommending an appropriate shop type for a given location. Experimental results show that the proposed method outperforms state-of-the-art solutions.

Original languageEnglish
Title of host publicationUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages161-164
Number of pages4
ISBN (Electronic)9781450335751
DOIs
StatePublished - 7 Sep 2015
EventACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015 - Osaka, Japan
Duration: 7 Sep 201511 Sep 2015

Publication series

NameUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers

Conference

ConferenceACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015
Country/TerritoryJapan
CityOsaka
Period7/09/1511/09/15

Keywords

  • Location-based services
  • Shop type recommendation
  • Social media

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