Post-Filtering for a Restaurant Context-Aware Recommender System

  • Xochilt Ramirez-Garcia
  • Mario García-Valdez
Part of the Studies in Computational Intelligence book series (SCI, volume 547)


Nowadays recommender systems are successfully used in various fields. One application is the recommendation of restaurants, where even if the method of customer service is the same, the quality of service varies depending on the resources invested to improve it. Traditionally, in a restaurant a waiter takes orders from customers and then delivers the product. The motivation of this work is to make recommendations of restaurants with the aim of disseminating information about products and services offered by restaurants in the city of Tijuana through a Web based platform. The proposed recommendation algorithm is based on contextual post-filtering approach, using the output of a collaborative filtering algorithm together with contextual information of the user’s current situation. The dataset used was explicitly acquired through questionnaires answered by 50 users; and the experiment was performed with a data set of 1,422 ratings of 50 users and 40 restaurants. We evaluate our approach with Mean Absolute Error (MAE) using dataset obtained of the questionnaire and the experimental results show that our approach has an acceptable accuracy for the dataset used.


Recommender systems Context-aware Collaborative filtering, recommendations 


  1. 1.
    Campochiaro, E., Cassata, R., Cremonesi, P., Turrin, R.: Do metrics make recommender algorithms? In: International Conference on Advanced Information Networking and Applications Workshops. Milano (2009)Google Scholar
  2. 2.
    Ramirez-Garcia, X., Garcia-Valdez, M.: Restaurant recommendations based on a domain model and fuzzy rules. In: International Seminary in Computer Intelligence (ISCI). Tijuana Institute of Technology, Tijuana (2012)Google Scholar
  3. 3.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context-Aware Places of Interest Recommendations and Explanations. Free University of Bozen-Bolzano, Bolzano (2011)Google Scholar
  4. 4.
    Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assesment and exploitation in mobile recommender systems. In: Personal and Ubiquitous Computing. Free University of Bolzano, Bolzano (2011)Google Scholar
  5. 5.
    Martinez, L., Calles, J., Martin, E.: Ontology-based Web service to recommend spare time activities. In: International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec), Barcelona (2010)Google Scholar
  6. 6.
    Kim, K.R., Lee, J.H., Byeon, J.H.: Recommender system using the movie genre similarity in mobile service. In: 4th Internartional Conference on Multimedia and Ubiquitous Engineering (MUE) (2010)Google Scholar
  7. 7.
    Zimmermann, M., Lorenz, A.: Personalization and context management. User Model. User-Adap. Inter. 15(3–4), 275–302 (2005)CrossRefGoogle Scholar
  8. 8.
    Baltrunas L., Ricci F.: Context-based splitting of item ratings in collaborative filtering. In: The ACM Conference Series on Recommender Systems (2009)Google Scholar
  9. 9.
    Kahng, M., Lee, S., Lee, S.-G.: Ranking in Context-Aware Recommender System. School of Computer Science and Engineering. ACM, Hyderabad (2011)Google Scholar
  10. 10.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRefGoogle Scholar
  11. 11.
    Zheng Y., Li L., Zheng F.: Context-awareness support for content recommendation in e-learning environments. In: International Conference on Information Management, Innovation Management and Industrial Engineering (2009)Google Scholar
  12. 12.
    Dey, A.K., Abowd, G.D.: Towards a better understanding of context and context-awareness. Graphics, Visualization and Usability Center and College of Computing. Georgia Institute of Technology, Atlanta (1999)Google Scholar
  13. 13.
    Fischer, Gerhard: Context-Aware Systems—The Right Information, at the Right Time, in the Right Place, in the Right Way, to the Rigth Person. ACM University of Colorado, Boulder USA (2012)Google Scholar
  14. 14.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Racci, F., et al. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011)CrossRefGoogle Scholar
  15. 15.
    Caraciolo, M.: Artificial intelligence in motion. Fecha de consulta: noviembre, 2013. (2013)
  16. 16.
    Shani, G., Gunawardana, A.: Evaluating Recommendation Systems, Microsoft. Springer, New York (2009)Google Scholar
  17. 17.
    Romadhony, A., Al Faraby, S., Pudjoatmodjo, B.: Online shopping recommender system using hybrid method. In: International Conference of Information and Communication Technology (ICoICT) (2013)Google Scholar
  18. 18.
    Devi, M.K.D., Samy, R.T., Kumar, S.V., Venkatesh, P.: Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems. In: International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4 (2010)Google Scholar
  19. 19.
    Chu, C.H., Wu, S.H.: A Chinese restaurant recommendation system based on mobile context-aware services. In: IEEE 14th International Conference on Mobile Data Management. Taichung University, Taichung (2013)Google Scholar
  20. 20.
    Haddad, M.R., Baazaoui, H., Ziou, D., Ben Ghézala, H.: Towards a new model for context-aware recommendation. In: Department of Informatics. University of Sherbrooke, Québec (2012)Google Scholar
  21. 21.
    Baltrunas, L., Ludwig, B., Ricci, F.: Context relevance assessment for recommender systems. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 287–290 (2011)Google Scholar
  22. 22.
    Ramaswamy, L., et al.: CAESAR: a context-aware, social recommender system for low-end mobile devices. In: IEEE. International Conference on Mobile Data Management: Systems, and Services and Middleware (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Instituto Tecnológico de TijuanaTijuanaMexico

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