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. JungEmail author


With remarkable successes of sharing economy services (e.g., UBER (, Airbnb (, 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.


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



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


  1. 1.
    Hamari J, Sjöklint M, Ukkonen A (2015) The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology 67(9):2047–2059CrossRefGoogle Scholar
  2. 2.
    Hong M, Jung JJ (2018) Multi-sided recommendation based on social tensor factorization. Inf Sci 447:140–156CrossRefGoogle Scholar
  3. 3.
    Chen L, Nayak R, Xu Y (2010) Improving matching process in social network. In: Proceeding of the 2010 IEEE international conference on data mining workshops. ICDMW, Sydney, pp 305–311Google Scholar
  4. 4.
    Park YJ (2013) An adaptive match-making system reflecting the explicit and implicit preferences of users. Expert Syst Appl 40(4):1196–1204MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tu K, Ribeiro B, Jensen D, Towsley D, Liu B, Jiang H, Wang X (2014) Online dating recommendations: matching markets and learning preferences. In: Chung C, Broder AZ, Shim K, Suel T (eds) Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion, pp 787–792Google Scholar
  6. 6.
    Cai X, Bain M, Krzywicki A, Wobcke W, Kim YS, Compton P, Mahidadia A (2010) Learning collaborative filtering and its application to people to people recommendation in social networks. In: Proceeding of the 2010 IEEE international conference on data mining (ICDM 2010). IEEE, Sydney, pp 743–748Google Scholar
  7. 7.
    Kutty S, Nayak R, Chen L (2013) A people-to-people matching system using graph mining techniques. World Wide Web 17(3):311–349CrossRefGoogle Scholar
  8. 8.
    Hu S, Zhu C, Xie F, Xiao K (2015) A recommendation algorithm for two-sided matching problems based on f-score. In: Proceeding of the 2015 IEEE international conference on computer and communications (ICCC 2015). IEEE, Chengdu, pp 25–29Google Scholar
  9. 9.
    Bui KN, Jung JJ (2018) Internet of agents framework for connected vehicles: A case study on distributed traffic control system. J Parallel Distrib Comput 116:89–95CrossRefGoogle Scholar
  10. 10.
    Ma S, Zheng Y, Wolfson O (2013) T-share: A Large-scale dynamic taxi ridesharing service. In: Proceedings of the 2013 IEEE 29th International Conference on Data Engineering. ICDE, Brisbane, pp 410–421Google Scholar
  11. 11.
    Pelzer D, Xiao J, Zehe D, Lees MH, Knoll AC, Aydt H (2015) A partition-based match making algorithm for dynamic ridesharing. IEEE Trans Intell Transp Syst 16(5):2587–2598CrossRefGoogle Scholar
  12. 12.
    Hong M, Jung JJ, Camacho D (2017) GRSAT: A novel method on group recommendation by social affinity and trustworthiness. Cybern Syst 48(3):140–161CrossRefGoogle Scholar
  13. 13.
    Hong M, Jung JJ, Piccialli F, Chianese A (2017) Social recommendation service for cultural heritage. Pers Ubiquit Comput 21(2):191–201CrossRefGoogle Scholar
  14. 14.
    Parambath SAP, Usunier N, Grandvalet Y (2016) A coverage-based approach to recommendation diversity on similarity graph. In: Sen S, Geyer W, Freyne J, Castells P (eds) Proceedings of the 10th ACM Conference on Recommender Systems - RecSys’16. ACM Press, Boston, pp 15–22Google Scholar
  15. 15.
    Teo CH, Nassif H, Hill D, Srinivasan S, Goodman M, Mohan V, Vishwanathan S (2016) Adaptive, personalized diversity for visual discovery. In: Sen S, Geyer W, Freyne J, Castells P (eds) Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16. ACM Press, Boston, pp 35–38Google Scholar
  16. 16.
    Wasilewski J, Hurley N (2016) Intent-aware diversification using a constrained PLSA. In: Sen S, Geyer W, Freyne J, Castells P (eds) Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16, pp 39–42Google Scholar
  17. 17.
    Park YJ, Tuzhilin A (2008) The long tail of recommender systems and how to leverage it. In: Pu P, Bridge DG, Mobasher B, Ricci F (eds) Proceedings of the 2008 ACM conference on Recommender systems - RecSys '08, pp 11–18Google Scholar
  18. 18.
    Yao L, Sheng QZ, Qin Y, Wang X, Shemshadi A, He Q (2015) Context-aware point-of-interest recommendation using tensor factorization with social regularization. In: Baeza-Yates RA, Lalmas M, Moffat A, Ribeiro-Neto BA (eds) Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15, Santiago, Chile, pp 1007–1010Google Scholar
  19. 19.
    Rafailidis D, Axenopoulos A, Etzold J, Manolopoulou S, Daras P (2014) Content-based tag propagation and tensor factorization for personalized item recommendation based on social tagging. ACM Transactions on Interactive Intelligent Systems 3(4):1–27CrossRefGoogle Scholar
  20. 20.
    Symeonidis P (2016) Matrix and tensor decomposition in recommender systems. In: Sen S, Geyer W, Freyne J, Castells P (eds) Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16, pp 429–430Google Scholar
  21. 21.
    Symeonidis P, Zioupos A (2016) Matrix and Tensor Factorization Techniques for Recommender Systems. Springer International Publishing, New YorkCrossRefzbMATHGoogle Scholar
  22. 22.
    Gale D, Shapley LS (1962) College admissions and the stability of marriage. Am Math Mon 69(1):386–391MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Gale D (2001) The two-sided matching problem: origin, development and current issues. International Game Theory Review 03(2 & 3):237–252MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Orgaz GB, Jung JJ, Camacho D (2016) Social big data: Recent achievements and new challenges. Information Fusion 28:45– 59CrossRefGoogle Scholar
  25. 25.
    Zhang A, Bhardwaj A, Karger D (2016) Confer: A conference recommendation and meetup tool. In: Bjørn P, Konstan JA, Gergle D, Morris M R (eds) Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion - CSCW '16 Companion, pp 118–121Google Scholar

Copyright information

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

Authors and Affiliations

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

Personalised recommendations