Leveraging Geographic Feature Embeddings for Enhanced Location-Based Recommendation Systems
Resumo
Geographically-aware models are becoming increasingly important in Points of Interest (POI) Recommendation Systems (RSs), particularly with the rise of Location-Based Systems and Social Networks, benefiting various areas and enhancing user experience and engagement. Although current POI RSs are of good quality, they often overlook intrinsic geographic features such as nearby rivers, buildings, and streets in POI’s vicinity, which can significantly influence user preferences. In this study, we propose and evaluate the use of POI type geographic embeddings that incorporate geographic features to enhance POI RSs. The results indicate that this approach improves the effectiveness of POI recommender models.
Palavras-chave:
Recommendation Systems, Points of Interest, Geographic Embeddings, Location-Based Systems
Referências
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Liu, B., Su, Y., Zha, D., Gao, N., and Xiang, J. (2019). Carec: Content-aware point-of-interest recommendation via adaptive bayesian personalized ranking. Aust. J. Intell. Inf. Process. Syst., 15(3):61–68.
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Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In Rappa, M., Jones, P., Freire, J., and Chakrabarti, S., editors, Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010, pages 811–820. ACM.
Saito, T. and Sato-Shimokawara, E. (2023). Music recommender system considering the variations in music selection criterion using an interactive genetic algorithm. In Saeed, K., Dvorský, J., Nishiuchi, N., and Fukumoto, M., editors, Computer Information Systems and Industrial Management - 22nd International Conference, CISIM 2023, Tokyo, Japan, September 22-24, 2023, Proceedings, volume 14164 of Lecture Notes in Computer Science, pages 382–393. Springer.
Shi, M., Shen, D., Kou, Y., Nie, T., and Yu, G. (2021). Attentional memory network with correlation-based embedding for time-aware POI recommendation. Knowl. Based Syst., 214:106747.
Silva, S. D., Campelo, C. E. C., and de Oliveira, M. G. (2023). POI types characterization based on geographic feature embeddings. In Hong, J., Lanperne, M., Park, J. W., Cerný, T., and Shahriar, H., editors, Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023, pages 507–514. ACM.
Wang, X., Liu, X., Li, L., Chen, X., Liu, J., and Wu, H. (2021). Time-aware user modeling with check-in time prediction for next POI recommendation. In Chang, C. K., Daminai, E., Fan, J., Ghodous, P., Maximilien, M., Wang, Z., Ward, R., and Zhang, J., editors, 2021 IEEE International Conference on Web Services, ICWS 2021, Chicago, IL, USA, September 5-10, 2021, pages 125–134. IEEE.
Wang, X., Liu, Y., Zhou, X., Wang, X., and Leng, Z. (2022). A point-of-interest recommendation method exploiting sequential, category and geographical influence. ISPRS Int. J. Geo Inf., 11(2):80.
Wang, Z., Li, H., and Rajagopal, R. (2020). Urban2vec: Incorporating street view imagery and pois for multi-modal urban neighborhood embedding. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 1013–1020. AAAI Press.
Wu, Y., Li, K., Zhao, G., and Qian, X. (2022). Personalized long- and short-term preference learning for next POI recommendation. IEEE Trans. Knowl. Data Eng., 34(4):1944–1957.
Yan, B., Janowicz, K., Mai, G., and Gao, S. (2017). From ITDL to place2vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In Hoel, E. G., Newsam, S. D., Ravada, S., Tamassia, R., and Trajcevski, G., editors, Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017, Redondo Beach, CA, USA, November 7-10, 2017, pages 35:1–35:10. ACM.
Yan, X., Song, T., Jiao, Y., He, J., Wang, J., Li, R., and Chu, W. (2023). Spatio-temporal hypergraph learning for next POI recommendation. In Chen, H., Duh, W. E., Huang, H., Kato, M. P., Mothe, J., and Poblete, B., editors, Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 403–412. ACM.
Yang, S., Liu, J., and Zhao, K. (2022). Getnext: Trajectory flow map enhanced transformer for next POI recommendation. In Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J. S., and Kazai, G., editors, SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, pages 1144–1153. ACM.
Yin, F., Liu, Y., Shen, Z., Chen, L., Shang, S., and Han, P. (2023). Next POI recommendation with dynamic graph and explicit dependency. In Williams, B., Chen, Y., and Neville, J., editors, Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pages 4827–4834. AAAI Press.
Yuan, Q., Cong, G., Ma, Z., Sun, A., and Magnenat-Thalmann, N. (2013). Time-aware point-of-interest recommendation. In Jones, G. J. F., Sheridan, P., Kelly, D., de Rijke, M., and Sakai, T., editors, The 36th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’13, Dublin, Ireland - July 28 - August 01, 2013, pages 363–372. ACM.
Zhang, H., Bai, W., Ding, J., and Jin, J. (2023). Time-aware POI recommendation based on multi-grained location grouping. In Shen, W., Barthès, J. A., Luo, J., Vivacqua, A. S., Schneider, D., Xie, C., Zhang, J., Zhu, H., Peng, K., and da Motta, C. L. R., editors, 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023, Rio de Janeiro, Brazil, May 24-26, 2023, pages 1796–1801. IEEE.
Zhao, P., Luo, A., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., and Zhou, X. (2022). Where to go next: A spatio-temporal gated network for next POI recommendation. IEEE Trans. Knowl. Data Eng., 34(5):2512–2524.
Zhao, S., Zhao, T., Yang, H., Lyu, M. R., and King, I. (2016). STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In Schuurmans, D. and Wellman, M. P., editors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 315–322. AAAI Press.
Davtalab, M. and Alesheikh, A. A. (2021). A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowl. Inf. Syst., 63(1):65–85.
Feng, S., Cong, G., An, B., and Chee, Y. M. (2017). Poi2vec: Geographical latent representation for predicting future visitors. In Singh, S. and Markovitch, S., editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 102–108. AAAI Press.
Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y. M., and Yuan, Q. (2015). Personalized ranking metric embedding for next new POI recommendation. In Yang, Q. and Wooldridge, M. J., editors, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 2069–2075. AAAI Press.
Feng, S., Tran, L. V., Cong, G., Chen, L., Li, J., and Li, F. (2020). HME: A hyperbolic metric embedding approach for next-poi recommendation. In Huang, J. X., Chang, Y., Cheng, X., Kamps, J., Murdock, V., Wen, J., and Liu, Y., editors, Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 1429–1438. ACM.
Halder, S., Lim, K. H., Chan, J., and Zhang, X. (2021). Transformer-based multi-task learning for queuing time aware next POI recommendation. In Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R. K., Reddy, P. K., Srivastava, J., and Chakraborty, T., editors, Advances in Knowledge Discovery and Data Mining - 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part II, volume 12713 of Lecture Notes in Computer Science, pages 510–523. Springer.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Comput., 9(8):1735–1780.
Koren, Y., Bell, R. M., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30–37.
Kulkarni, A., Powell, L., Murphy, S., Rao, N., and Chu, S. L. (2023). Everyday-inspired movies: Towards the design of movie recommender systems based on everyday life through personal social media. In Abdelnour-Nocera, J. L., Lárusdóttir, M. K., Petrie, H., Piccinno, A., and Winckler, M., editors, Human-Computer Interaction - INTERACT 2023 - 19th IFIP TC13 International Conference, York, UK, August 28 - September 1, 2023, Proceedings, Part III, volume 14144 of Lecture Notes in Computer Science, pages 160–169. Springer.
Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Syst. Appl., 41(4):2065–2073.
Liu, B., Su, Y., Zha, D., Gao, N., and Xiang, J. (2019). Carec: Content-aware point-of-interest recommendation via adaptive bayesian personalized ranking. Aust. J. Intell. Inf. Process. Syst., 15(3):61–68.
Liu, Q., Wu, S., Wang, L., and Tan, T. (2016). Predicting the next location: A recurrent model with spatial and temporal contexts. In Schuurmans, D. and Wellman, M. P., editors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 194–200. AAAI Press.
Luo, Y., Liu, Q., and Liu, Z. (2021). STAN: spatio-temporal attention network for next location recommendation. In Leskovec, J., Grobelnik, M., Najork, M., Tang, J., and Zia, L., editors, WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 2177–2185. ACM / IW3C2.
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. In Bengio, Y. and LeCun, Y., editors, 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings.
Qin, Y., Wang, Y., Sun, F., Ju, W., Hou, X., Wang, Z., Cheng, J., Lei, J., and Zhang, M. (2023). Disenpoi: Disentangling sequential and geographical influence for point-of-interest recommendation. In Chua, T., Lauw, H. W., Si, L., Terzi, E., and Tsaparas, P., editors, Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023, pages 508–516. ACM.
Rendle, S., Freudenthaler, C., and Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In Rappa, M., Jones, P., Freire, J., and Chakrabarti, S., editors, Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010, pages 811–820. ACM.
Saito, T. and Sato-Shimokawara, E. (2023). Music recommender system considering the variations in music selection criterion using an interactive genetic algorithm. In Saeed, K., Dvorský, J., Nishiuchi, N., and Fukumoto, M., editors, Computer Information Systems and Industrial Management - 22nd International Conference, CISIM 2023, Tokyo, Japan, September 22-24, 2023, Proceedings, volume 14164 of Lecture Notes in Computer Science, pages 382–393. Springer.
Shi, M., Shen, D., Kou, Y., Nie, T., and Yu, G. (2021). Attentional memory network with correlation-based embedding for time-aware POI recommendation. Knowl. Based Syst., 214:106747.
Silva, S. D., Campelo, C. E. C., and de Oliveira, M. G. (2023). POI types characterization based on geographic feature embeddings. In Hong, J., Lanperne, M., Park, J. W., Cerný, T., and Shahriar, H., editors, Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023, pages 507–514. ACM.
Wang, X., Liu, X., Li, L., Chen, X., Liu, J., and Wu, H. (2021). Time-aware user modeling with check-in time prediction for next POI recommendation. In Chang, C. K., Daminai, E., Fan, J., Ghodous, P., Maximilien, M., Wang, Z., Ward, R., and Zhang, J., editors, 2021 IEEE International Conference on Web Services, ICWS 2021, Chicago, IL, USA, September 5-10, 2021, pages 125–134. IEEE.
Wang, X., Liu, Y., Zhou, X., Wang, X., and Leng, Z. (2022). A point-of-interest recommendation method exploiting sequential, category and geographical influence. ISPRS Int. J. Geo Inf., 11(2):80.
Wang, Z., Li, H., and Rajagopal, R. (2020). Urban2vec: Incorporating street view imagery and pois for multi-modal urban neighborhood embedding. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 1013–1020. AAAI Press.
Wu, Y., Li, K., Zhao, G., and Qian, X. (2022). Personalized long- and short-term preference learning for next POI recommendation. IEEE Trans. Knowl. Data Eng., 34(4):1944–1957.
Yan, B., Janowicz, K., Mai, G., and Gao, S. (2017). From ITDL to place2vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In Hoel, E. G., Newsam, S. D., Ravada, S., Tamassia, R., and Trajcevski, G., editors, Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2017, Redondo Beach, CA, USA, November 7-10, 2017, pages 35:1–35:10. ACM.
Yan, X., Song, T., Jiao, Y., He, J., Wang, J., Li, R., and Chu, W. (2023). Spatio-temporal hypergraph learning for next POI recommendation. In Chen, H., Duh, W. E., Huang, H., Kato, M. P., Mothe, J., and Poblete, B., editors, Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 403–412. ACM.
Yang, S., Liu, J., and Zhao, K. (2022). Getnext: Trajectory flow map enhanced transformer for next POI recommendation. In Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J. S., and Kazai, G., editors, SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, pages 1144–1153. ACM.
Yin, F., Liu, Y., Shen, Z., Chen, L., Shang, S., and Han, P. (2023). Next POI recommendation with dynamic graph and explicit dependency. In Williams, B., Chen, Y., and Neville, J., editors, Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pages 4827–4834. AAAI Press.
Yuan, Q., Cong, G., Ma, Z., Sun, A., and Magnenat-Thalmann, N. (2013). Time-aware point-of-interest recommendation. In Jones, G. J. F., Sheridan, P., Kelly, D., de Rijke, M., and Sakai, T., editors, The 36th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR ’13, Dublin, Ireland - July 28 - August 01, 2013, pages 363–372. ACM.
Zhang, H., Bai, W., Ding, J., and Jin, J. (2023). Time-aware POI recommendation based on multi-grained location grouping. In Shen, W., Barthès, J. A., Luo, J., Vivacqua, A. S., Schneider, D., Xie, C., Zhang, J., Zhu, H., Peng, K., and da Motta, C. L. R., editors, 26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023, Rio de Janeiro, Brazil, May 24-26, 2023, pages 1796–1801. IEEE.
Zhao, P., Luo, A., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., and Zhou, X. (2022). Where to go next: A spatio-temporal gated network for next POI recommendation. IEEE Trans. Knowl. Data Eng., 34(5):2512–2524.
Zhao, S., Zhao, T., Yang, H., Lyu, M. R., and King, I. (2016). STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In Schuurmans, D. and Wellman, M. P., editors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 315–322. AAAI Press.
Publicado
14/10/2024
Como Citar
LEITE, Nícolas Moreira Nobre; E. C. CAMPELO, Claudio; DANTAS SILVA, Salatiel.
Leveraging Geographic Feature Embeddings for Enhanced Location-Based Recommendation Systems. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 354-366.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240809.