A Collaborative Location-Based Personalized Recommender System

  • Madhusree Kuanr
  • Sachi Nandan Mohanty
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


With the rapid development of information and communication technology, numbers of tourists are increasing all over the world due to the easy way to plan for the tour. Location-based recommender system considers both user’s behavior and preference for recommendation process. In this paper, we have proposed a location-based personalized recommender system which offers a set of spots to the tourist by considering the place, food, and product preference of the tourists. The proposed system uses collaborative filtering technique to recommend the best spots along with food availability and product availability to the tourist according to the opinions of the local users who already visited those spots. Cosine similarity measure is used to find the local users who are similar to the given query user. The results revealed that collaborative filtering is the more reliable technique for personalized recommender systems. The proposed system is evaluated in terms of precision, recall, and f-measure values.


Recommender systems Collaborative filtering Location-based Cosine similarity 


  1. 1.
    García-Cumbreras, M. Á., Montejo-Ráez, A., & Díaz-Galiano, M. C. (2013). Pessimists and optimists: Improving collaborative filtering through sentiment analysis. Expert Systems with Applications, 40(17), 6758–6765.Google Scholar
  2. 2.
    Herrera-Viedma, E., Herrera, F., Martínez, L., Herrera, J., & López (2004), A. Incorporating filtering techniques in a fuzzy linguistic multi-agent model for information gathering on the web. Fuzzy Sets and Systems, 148, 61–83.Google Scholar
  3. 3.
    Schafer, J., Konstan, J., & Riedl, J. (2002): Meta-recommendation system: user-controlled integration of diverse recommendations. In Proceedings of the eleventh international conference on information and knowledge management, 43–51.Google Scholar
  4. 4.
    Inma Garcia, Laura Sebastia, Sergio Pajares, & Eva Onaindia. (2011): The Generalist recommender System GRSK and Its Extension to Groups. Web Information Systems and Technologies, 215–229.Google Scholar
  5. 5.
    Kakaletris, G., Varoutas, D., Katsianis, D., Sphicopoulos, T., & Kouvas. (2004): Designing & Implementing an open infrastructure for location-based tourism related content delivery. Wireless Personal Communications, 30, (2), 153–165.Google Scholar
  6. 6.
    Dickson K. W. Chiu & Ho-fung Leung. (2005): Towards ubiquitous tourists service coordination and integration: a multi-agent and semantic web approach. In Proceedings of the 7th international conference on Electronic commerce (IECE ’05). ACM, New York, NY, USA, 574–581.Google Scholar
  7. 7.
    Miguel Á. García-Cumbreras, Arturo Montejo-Ráez & Manuel C. Díaz-Galiano. (2013): Pessimists and optimists: Improving collaborative filtering through sentiment analysis. Expert Systems with Applications. 40 (17), 6758–6765.Google Scholar
  8. 8.
    Veningston. K, R. Shanmugalakshmi. (2015): Personalized Location aware Recommendation System, International Conference on Advanced Computing and Communication Systems (ICACCS -2015), Jan. 05–07, 2015, Coimbatore, INDIA.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUtkal UniversityBhubaneswarIndia
  2. 2.Deaprtment of Computer Science & Engg.Gandhi Institute for TechnologyBhubaneswarIndia

Personalised recommendations