Cluster Computing

, Volume 22, Supplement 3, pp 6069–6077 | Cite as

Research on O2O take-away restaurant recommendation system: taking APP as an example

  • Linan WangEmail author
  • Bo Yi


Online to Offline (O2O) take-away has the great potentialT for development in China, but with a large number of merchants taking part in O2O take-away APP, resulting in the problem of information overload. And the recommended system can effectively relieve the information overload of APP. In this paper, the O2O take-away restaurant recommendation system is studied in detail. By analyzing the current situation of the O2O takeaway restaurant recommendation system, we put forward a recommendation algorithm based on rank-centroid/analytic hierarchy process. Through transforming the recent booking preferences of user into the take-away service standard weight, we establish the model, combined with the case of APP, compute actual scores of the restaurants by the comprehensive score, and choose the high score of the restaurants to recommend. Compared with the mainstream collaborative filtering recommendation method, the proposed method is simple and computationally smaller.


O2O take-away restaurant recommendation system RC/AHP Dynamic APP 


  1. 1.
    Woosuk, K., Chung, S., Bae, Y.H.: O2O trend and future: focused on difference from each case. J. Mark. Thought 3(4), 49–66 (2017)Google Scholar
  2. 2.
    Rana, C., Jain, S.K.: A study of the dynamic features of recommender systems. Artif. Intell. Rev. 43(1), 141–153 (2015)CrossRefGoogle Scholar
  3. 3.
    Adomaviciu, S.G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  4. 4.
    Su, J.H., Chang, W.Y., Tseng, V.S.: Effective social content-based collaborative filtering for music recommendation. Intell. Data Anal. 21, 195–216 (2017)CrossRefGoogle Scholar
  5. 5.
    Gopalan, P.K., Charli, L., David, B.: Content-based recommendations with Poisson factorization. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), pp. 3176–3184 (2014)Google Scholar
  6. 6.
    Yap, G.E., Tan, A.H., Pang, H.H.: Discovering and exploiting causal dependencies for robust mobile context-aware recommenders. IEEE Trans. Knowl.Data Eng. 19(7), 977–992 (2007)CrossRefGoogle Scholar
  7. 7.
    Lei, T., Guoheng, R., Wei, Wang: Clustering optimization personalized book recommendation based on collaborative filtering. Res. Library Sci. 08, 75–80 (2017)Google Scholar
  8. 8.
    Khatami, M., Pashazadeh, S.: Enhancing prediction in collaborative filtering-based recommender systems. Int. J. Comput. Sci. Eng. 2(1), 48–51 (2014)Google Scholar
  9. 9.
    Xiangjie, Q., Lingyun, Z.: Overseas applied studies on travel recommender systems in the past ten years. Tour. Tribune 29(8), 117–127 (2014)Google Scholar
  10. 10.
    Li, M., Meng, X., Chen, Y., Zheng, J., Li, Q.: Research and implementation of online ordering recommendation algorithm. Technol. Econ. Guide 12, 9–11 (2017)Google Scholar
  11. 11.
    Ma, J., Chen, H., Stephan, R.M.: Recommending services via hybrid recommendation algorithms and hidden Markov model in cloud. J. Central South Univ. (Science and Technology) 47(1), 81–90 (2016)Google Scholar
  12. 12.
    Guo, Y.: Research and application of personalized recommendation algorithm for O2O user behavior Analysis. University Of Hebei (2017)Google Scholar
  13. 13.
    Chen, W., Wally, J.S.: Performance assessment method of online auditing: combined use between RC and AHP. Syst. Eng. Theory Pract. 32(8), 1768–1776 (2012)Google Scholar
  14. 14.
    Barron, F.H., Barrett, B.E.: Decision quality using randed attribute weights. Manag. Sci. 42(11), 1515–1523 (1996)zbMATHCrossRefGoogle Scholar
  15. 15.
    Dey, D., Sarker, S., De, P.: A distance-based approach to entity reconcilation in heterogeneous database. IEEE Trans. Knowl. Data Eng. 14(3), 567–582 (2002)CrossRefGoogle Scholar
  16. 16.
    Licai, W., Xiangwu, M., Yujie, Zhang: Context-aware recommender systems. J. Softw. 23(1), 1–20 (2012)CrossRefGoogle Scholar
  17. 17.
    Jung, S.Y., Hong, J.H., Kim, T.S.: A statistical model for user preference. IEEE Trans. Knowl. data Eng. 17(6), 834–843 (2005)CrossRefGoogle Scholar
  18. 18.
    Holland, S., Kießling, W.: Situated preferences and preference repositories for personalized database applications. In: 23rd International Conference on Conceptual (ER 2004) Conceptual Modeling-ER 2004, pp. 511–523 (2004)Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Jilin University of Finance and EconomicsChangchunChina

Personalised recommendations