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Mobile Networks and Applications

, Volume 23, Issue 4, pp 1089–1096 | Cite as

Owner-Borrower Model for Recommenders in O2O Services

  • O-Joun Lee
  • Jai E. Jung
Article
  • 27 Downloads

Abstract

With remarkable successes of sharing economy services (e.g., UBER (https://www.uber.com), Airbnb (https://www.airbnb.com), and so on), the amount of items which are distributed through these services is rapidly increasing. Therefore recommender systems for the sharing economy services are required. However, the existing recommenders are hard to support the sharing economy services, since they have focused on a ‘Item-User’ model that the recommenders provide satisfiable items to consumers (users) in accordance with only the consumers’ preferences. In this regard, we suggest a novel recommendation model, ‘Owner-Borrower’ model which considers the preferences of both sides: owners and borrowers of properties (items). Also, we propose a recommendation method based on the proposed model by applying a tensor factorization method and the Gale-Shapley algorithm. The tensor factorization is used for estimating preferences of the owners and the borrowers. With the estimated preferences, the Gale-Shapley algorithm makes optimal matches between the borrowers and the owners’ properties.

Keywords

Sharing economy service Recommender system Match-making recommendation Owner-borrower model 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A2B4010774).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringChung-Ang UniversityChung-AngKorea

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