Paper Information | SBC BOOKS

#	Name
1	Nicolas Moreira Nobre Leite(nicolas.leite@ccc.ufcg.edu.br)
2	Claudio Elízio Calazans Campelo(campelo@computacao.ufcg.edu.br)
3	Salatiel Dantas Silva(salatiel.dantas@computacao.ufcg.edu.br)

#	Reference
1	Bhuvanya, R. and Kavitha, M. (2023). A real-time e-commerce accessories recom- mender system by coupling deep learning and histogram features. J. Intell. Fuzzy Syst., 45(1):1179–1193.
2	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.
3	Feng, S., Cong, G., An, B., and Chee, Y. M. (2017). Poi2vec: Geographical latent rep- resentation 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.
4	Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y. M., and Yuan, Q. (2015). Personalized rank- ing metric embedding for next new POI recommendation. In Yang, Q. and Wooldridge, M. J., editors, Proceedings of the Twenty-Fourth International Joint Conference on Ar- tificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 2069–2075. AAAI Press
5	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 Informa- tion Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 1429–1438. ACM
6	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, vol- ume 12713 of Lecture Notes in Computer Science, pages 510–523. Springer.
7	Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Comput., 9(8):1735–1780
8	Koren, Y., Bell, R. M., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30–37.
9	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 - INTER- ACT 2023 - 19th IFIP TC13 International Conference, York, UK, August 28 - Septem- ber 1, 2023, Proceedings, Part III, volume 14144 of Lecture Notes in Computer Sci- ence, pages 160–169. Springer.
10	Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Syst. Appl., 41(4):2065–2073.
11	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.
12	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., edi- tors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 194–200. AAAI Press.
13	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.
14	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.
15	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.
16	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.
17	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 Infor- mation 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.
18	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.
19	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.
20	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.
21	Wang, X., Liu, Y., Zhou, X., Wang, X., and Leng, Z. (2022). A point-of-interest recom- mendation method exploiting sequential, category and geographical influence. ISPRS Int. J. Geo Inf., 11(2):80.
22	Wang, Z., Li, H., and Rajagopal, R. (2020). Urban2vec: Incorporating street view im- agery 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 Sym- posium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 1013–1020. AAAI Press.
23	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.
24	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 Tra- jcevski, G., editors, Proceedings of the 25th ACM SIGSPATIAL International Confer- ence on Advances in Geographic Information Systems, GIS 2017, Redondo Beach, CA, USA, November 7-10, 2017, pages 35:1–35:10. ACM.
25	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 Interna- tional ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 403–412. ACM.
26	Yang, S., Liu, J., and Zhao, K. (2022). Getnext: Trajectory flow map enhanced trans- former for next POI recommendation. In Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J. S., and Kazai, G., editors, SIGIR ’22: The 45th Interna- tional ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, pages 1144–1153. ACM.
27	Yin, F., Liu, Y., Shen, Z., Chen, L., Shang, S., and Han, P. (2023). Next POI recommen- dation 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.
28	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 - Au- gust 01, 2013, pages 363–372. ACM.
29	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.
30	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
31	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.

1

Bhuvanya, R. and Kavitha, M. (2023). A real-time e-commerce accessories recom- mender system by coupling deep learning and histogram features. J. Intell. Fuzzy Syst., 45(1):1179–1193.

2

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.

3

Feng, S., Cong, G., An, B., and Chee, Y. M. (2017). Poi2vec: Geographical latent rep- resentation 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.

4

Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y. M., and Yuan, Q. (2015). Personalized rank- ing metric embedding for next new POI recommendation. In Yang, Q. and Wooldridge, M. J., editors, Proceedings of the Twenty-Fourth International Joint Conference on Ar- tificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pages 2069–2075. AAAI Press

5

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 Informa- tion Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 1429–1438. ACM

6

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, vol- ume 12713 of Lecture Notes in Computer Science, pages 510–523. Springer.

7

Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Comput., 9(8):1735–1780

8

Koren, Y., Bell, R. M., and Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8):30–37.

9

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 - INTER- ACT 2023 - 19th IFIP TC13 International Conference, York, UK, August 28 - Septem- ber 1, 2023, Proceedings, Part III, volume 14144 of Lecture Notes in Computer Sci- ence, pages 160–169. Springer.

10

Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Syst. Appl., 41(4):2065–2073.

11

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.

12

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., edi- tors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, pages 194–200. AAAI Press.

13

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.

14

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.

15

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.

16

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.

17

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 Infor- mation 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.

18

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.

19

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.

20

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.

21

Wang, X., Liu, Y., Zhou, X., Wang, X., and Leng, Z. (2022). A point-of-interest recom- mendation method exploiting sequential, category and geographical influence. ISPRS Int. J. Geo Inf., 11(2):80.

22

Wang, Z., Li, H., and Rajagopal, R. (2020). Urban2vec: Incorporating street view im- agery 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 Sym- posium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 1013–1020. AAAI Press.

23

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.

24

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 Tra- jcevski, G., editors, Proceedings of the 25th ACM SIGSPATIAL International Confer- ence on Advances in Geographic Information Systems, GIS 2017, Redondo Beach, CA, USA, November 7-10, 2017, pages 35:1–35:10. ACM.

25

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 Interna- tional ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 403–412. ACM.

26

Yang, S., Liu, J., and Zhao, K. (2022). Getnext: Trajectory flow map enhanced trans- former for next POI recommendation. In Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J. S., and Kazai, G., editors, SIGIR ’22: The 45th Interna- tional ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022, pages 1144–1153. ACM.

27

Yin, F., Liu, Y., Shen, Z., Chen, L., Shang, S., and Han, P. (2023). Next POI recommen- dation 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.

28

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 - Au- gust 01, 2013, pages 363–372. ACM.

29

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.

30

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

31

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.

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