Where to Go: City Recommendation Based on User Communities

• Ruhan Bidart Federal University of Minas Gerais
• Adriano C. M. Pereira Federal University of Minas Gerais
• Jussara Almeida Federal University of Minas Gerais
• Anisio Lacerda Federal University of Minas Gerais

Abstract

Recommendation systems play a key role in the decision making process of users in online systems. In this work, we propose a city recommendation system that exploits the user interests and the similarity between different users. The proposed method builds a social network among users where the edges are weighted by the similarity of interests between pairs of users. This network is then used as a component of a collaborative filtering strategy. We evaluate our method using a large dataset collected from TripAdvisor. Our experimental results show that our approach can double the precision achieved by baseline approaches, which exploit only the overall popularity of cities, reaching 65%for the most active users.

Keywords: Recommender System, City Recommendation, User Communities

References

Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734–749.

Balabanovíc, M. and Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Commun. ACM, 40(3):66–72.

Cremonesi, P., Koren, Y., and Turrin, R. (2010). Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10, pages 39–46.

Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5):75 – 174.

Herlocker, J., Konstan, J. A., and Riedl, J. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr., 5(4):287–310.

Herlocker, J. L., Konstan, J. A., Terveen, L. G., John, and Riedl, T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22:5–53.

Jamali, M. and Ester, M. (2009). Using a trust network to improve top-n recommendation. In Proceedings of the Third ACM Conference on Recommender Systems, RecSys ’09, pages 181–188.

Kurashima, T., Iwata, T., Irie, G., and Fujimura, K. (2010). Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pages 579–588.

Linden, G., Smith, B., and York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76–80.

Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press.

Noulas, A., Scellato, S., Lathia, N., and Mascolo, C. (2012). A random walk around the city: New venue recommendation in location-based social networks. In Social- Com/PASSAT, pages 144–153. IEEE.

Ricci, F., Rokach, L., Shapira, B., and Kantor, P. B., editors (2011). Recommender Systems Handbook. Springer.

Schafer, J. B., Konstan, J. A., and Riedl, J. (2001). E-commerce recommendation applications. Data Min. Knowl. Discov., 5(1-2):115–153.

Wang, H., Terrovitis, M., and Mamoulis, N. (2013). Location recommendation in location-based social networks using user check-in data. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL’13, pages 374–383.

Yang, X., Steck, H., Guo, Y., and Liu, Y. (2012). On top-k recommendation using social networks. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys ’12, pages 67–74.