Combining complementary diversification models for personalized point-of-interest recommendations
Keywords:
Recommender systems, Point-of-interest, Personalization, Systematic mapping, FrameworkAbstract
Nowadays, Location-Based Social Networks (LBSNs) have become an important tool to help people explore new places -- a.k.a., points of interest (POIs). In this sense, this work: (1) performs a systematic mapping of the most recent approaches applied in LBSN; (2) presents a solution to the traditional accuracy-diversity trade-off of any recommender; and (3) provides such a solution through a reproducible package that also includes all the systematic mapping tools and several models of recommendation of POIs. Our solution is named DisCovER and it becomes a new benchmark for the scenario as it outperforms all state-of-the-art methods.
Downloads
References
Castillo, L., Armengol, E., Onaindı́a, E., Sebastiá, L., González, J., Rodrı́guez, A., Fernández, S., Arias, J. D., and Borrajo, D. (2008). Samap: An user-oriented adaptive system for planning tourist visits. Expert Systems with Applications, 34(2):1318–1332.
Dacrema, M. F., Boglio, S., Cremonesi, P., and Jannach, D. (2021). A troubling analysis of reproducibility and progress in recommender systems research. ACM Transactions on Information Systems (TOIS), 39(2):1–49.
Han, J. and Yamana, H. (2017). Geographical diversification in poi recommendation: toward improved coverage on interested areas. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 224–228. ACM.
Liu, B., Fu, Y., Yao, Z., and Xiong, H. (2013). Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1043–1051. ACM.
Lu, F. and Tintarev, N. (2018). A diversity adjusting strategy with personality for music recommendation. In IntRS@ RecSys, pages 7–14.
Puthiya Parambath, S. A., Usunier, N., and Grandvalet, Y. (2016). A coverage-based approach to recommendation diversity on similarity graph. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 15–22. ACM.
Qian, T., Liu, B., Nguyen, Q. V. H., and Yin, H. (2019). Spatiotemporal representation learning for translation-based poi recommendation. ACM Transactions on Information Systems (TOIS), 37(2):1–24.
Vargas, S. and Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, pages 109–116. ACM.
Werneck, H., Santos, R., Silva, N., Mourão, F., Pereira, A. C. M., and Rocha, L. (2021a). Effective and diverse poi recommendations through complementary diversification models. Expert Systems with Applications, 123:154–162.
Werneck, H., Silva, N., Mourão, F., Pereira, A. C. M., and Rocha, L. (2020a). Combining complementary diversification models for personalized poi recommendations. In Proceedings of the Brazilian Symposium on Multimedia and the Web, page 209–212.
Werneck, H., Silva, N., Pereira, A., Carvalho, M., Bellogı́n, A., Martinez-Gil, J., Mourão, F., and Rocha, L. (2022). A reproducible poi recommendation framework: Works mapping and benchmark evaluation. Information Systems, page 102019.
Werneck, H., Silva, N., Viana, M., Pereira, A. C., Mourão, F., and Rocha, L. (2021b). Points of interest recommendations: Methods, evaluation, and future directions. Information Systems, 101:101789.
Werneck, H., Silva, N., Viana, M. C., Mourão, F., Pereira, A. C., and Rocha, L. (2020b). A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 185–192.
