Explorando o nı́vel de atividade do usuário para melhorar a precisão dos sistemas de recomendação de pontos de interesse
Resumo
Sistemas de Recomendação (SsR) são aplicados em diversos cenários, tais como comércio eletrônico e, atualmente, em Redes Sociais Baseadas em Localização para recomendar pontos de interesse (POIs). Para cenário de POIs é necessário considerar a influência geográfica deles. As propostas atuais não alcançam resultados satisfatórios. Neste trabalho, abrimos uma nova perspectiva de pesquisa, propondo uma abordagem de pós-processamento que pode ser usada com qualquer SR. Medimos o nível de atividade dos usuários em diferentes subáreas de uma cidade e o usamos para reordenar os POIs recuperados. Avaliamos nossa proposta considerando seis SsR e três conjuntos de dados do Yelp, obtendo ganhos de até 15% de precisão.
Palavras-chave:
Sistemas de Recomendação, Pontos de Interesse
Referências
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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.
Liu, Y., Pham, T.-A. N., Cong, G., and Yuan, Q. (2017). An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment, 10(10):1010–1021.
Liu, Y., Wei, W., Sun, A., and Miao, C. (2014). Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 739–748. ACM.
Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74:12–32.
Miller, H. J. (2004). Tobler’s first law and spatial analysis. Annals of the Association of American Geographers, 94(2):284–289.
Silva, N., Carvalho, D., Pereira, A. C., Mour˜ao, F., and Rocha, L. (2019). The pure coldstart problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems, 80:1–12.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80–83.
Ye, M., Yin, P., and Lee, W.-C. (2010). Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pages 458–461. ACM.
Ye, M., Yin, P., Lee, W.-C., and Lee, D.-L. (2011). Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR, pages 325–334. ACM.
Yu, Y. and Chen, X. (2015). A survey of point-of-interest recommendation in locationbased social networks. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.
Yuan, Q., Cong, G., Ma, Z., Sun, A., and Thalmann, N. M. (2013). Time-aware point-ofinterest recommendation. In Proceedings of the 36th international ACM SIGIR, pages 363–372. ACM.
Zhang, J.-D. and Chow, C.-Y. (2015). Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th International ACM SIGIR, pages 443–452. ACM.
Bobadilla, J., Ortega, F., Hernando, A., and Guti´errez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46:109–132.
Fernandez-Tobıas, I., Tomeo, P., Cantador, I., Di Noia, T., and Di Sciascio, E. (2016). Accuracy and diversity in cross-domain recommendations for cold-start users with positive-only feedback. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 119–122. ACM.
Gao, H., Tang, J., Hu, X., and Liu, H. (2013). Exploring temporal effects for location recommendation on location-based social networks. In Proceedings of the 7th ACM conference on Recommender systems, pages 93–100. ACM.
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.
Kumar, S. S., Ramachandran, T., and Panboli, S. (2015). Product recommendations over facebook: the roles of influencing factors to induce online shopping. Asian Social Science, 11(2):202.
Li, X., Cong, G., Li, X.-L., Pham, T.-A. N., and Krishnaswamy, S. (2015). Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 433–442. ACM.
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., and Rui, Y. (2014). Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 831–840. 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.
Liu, Y., Pham, T.-A. N., Cong, G., and Yuan, Q. (2017). An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment, 10(10):1010–1021.
Liu, Y., Wei, W., Sun, A., and Miao, C. (2014). Exploiting geographical neighborhood characteristics for location recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pages 739–748. ACM.
Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74:12–32.
Miller, H. J. (2004). Tobler’s first law and spatial analysis. Annals of the Association of American Geographers, 94(2):284–289.
Silva, N., Carvalho, D., Pereira, A. C., Mour˜ao, F., and Rocha, L. (2019). The pure coldstart problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems, 80:1–12.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80–83.
Ye, M., Yin, P., and Lee, W.-C. (2010). Location recommendation for location-based social networks. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pages 458–461. ACM.
Ye, M., Yin, P., Lee, W.-C., and Lee, D.-L. (2011). Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR, pages 325–334. ACM.
Yu, Y. and Chen, X. (2015). A survey of point-of-interest recommendation in locationbased social networks. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.
Yuan, Q., Cong, G., Ma, Z., Sun, A., and Thalmann, N. M. (2013). Time-aware point-ofinterest recommendation. In Proceedings of the 36th international ACM SIGIR, pages 363–372. ACM.
Zhang, J.-D. and Chow, C.-Y. (2015). Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In Proceedings of the 38th International ACM SIGIR, pages 443–452. ACM.
Publicado
30/06/2020
Como Citar
CHAVES, Luiz Felipe ; SILVA, Nícollas ; ROCHA, Leonardo .
Explorando o nı́vel de atividade do usuário para melhorar a precisão dos sistemas de recomendação de pontos de interesse. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA DA SBC (CTIC-SBC), 39. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 41-50.
