Page 48 - 《软件学报》2020年第12期

3714 Journal of Software 软件学报 Vol.31, No.12, December 2020

用户和 POI 的语义特征会随着时间的变化而发生改变,即,不同时间节点所对应的用户与 POI 的语义特征
也会有不同.并且细致地区分不同时间段的语义特征,会使模型在提取用户特征和捕捉用户动态偏好时更加精
确,在一定程度上能够进一步提升模型的推荐性能.如何有效地提取不同时间段内用户与 POI 的语义特征,是进
一步研究时需要解决的问题.

References:
[1] Meng XW, Hu X, Wang LC, Zhang YJ. Mobile recommender systems and their applications. Ruanjian Xuebao/Journal of Software,
2013,24(1):91−108 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4292.htm [doi: 10.3724/SP.J.1001.2013.
04292]
[2] Yin H, Zhou X, Shao Y, Wang H, Sadiq S. Joint modeling of user check-in behaviors for point-of-interest recommendation. In:
Proc. of the 24th ACM Int’l on Conf. on Information and Knowledge Management. New York: ACM, 2015. 1631−1640. [doi: 10.
1145/2806416.2806500]
[3] Ren XY, Song MN, Song JD. Context-Aware point-of-interest recommendation in location-based social networks. Chinese Journal
of Computers, 2017,40(4):824−841 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2017.00824]
[4] Meng XW, Wang S, Ahmad W, Chen Y. A sequential multi-type POI recommendation service based on location. In: Proc. of the
5th Int’l Conf. on Identification, Information and Knowledge in the Internet of Things. Beijing: IEEE, 2016. 325−331. [doi:
10.1109/IIKI.2016.5]
[5] Lian D, Zhang Z, Ge Y, Zhang F, Yuan NJ, Xie X. Regularized content-aware tensor factorization meets temporal-aware location
recommendation. In: Proc. of the 16th IEEE Int’l Conf. on Data Mining. Barcelona: IEEE, 2016. 1029−1034. [doi: 10.1109/ICDM.
2016.0131]
[6] Liu Y, Ester M, Qian Y, Hu B, Cheung DW. Microscopic and macroscopic spatio-temporal topic models for check-in data. IEEE
Trans. on Knowledge and Data Engineering, 2017,29(9):1957−1970. [doi: 10.1109/TKDE.2017.2703825]
[7] Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of Machine Learning Research, 2003,3(Jan):993−1022.
[8] Ference G, Ye M, Lee WC. Location recommendation for out-of-town users in location-based social networks. In: Proc. of the 22nd
ACM Int’l Conf. on Information and Knowledge Management. New York: ACM, 2013. 721−726. [doi: 10.1145/2505515.2505637]
[9] Zhu WY, Peng WC, Chen LJ, Zheng K, Zhou XF. Modeling user mobility for location promotion in location-based social networks.
In: Proc. of the 21th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM, 2015. 1573−1582.
[doi: 10.1145/2783258.2783331]
[10] Wang W, Yin H, Chen L, Sun Y, Sadiq S, Zhou X. Location recommendation: A geographical sparse additive generative model for
spatial item recommendation. In: Proc of the 21th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New
York: ACM, 2015. 1255−1264. [doi: 10.1145/2783258.2783335]
[11] Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y. GeoMF: Joint geographical modeling and matrix factorization for point-of-interest
recommendation. In: Proc. of the 20th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM,
2014. 831−840. [doi: 10.1145/2623330.2623638]
[12] Li X, Cong G, Li X L, Pham TAN, Krishnaswamy S. Rank-GeoFM: A ranking based geographical factorization method for point of
interest recommendation. In: Proc. of the 38th Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval.
New York: ACM, 2015. 433−442. [doi: 10.1145/2766462.2767722]
[13] Griesner JB, Abdessalem T, Naacke H. POI recommendation: Towards fused matrix factorization with geographical and temporal
influences. In: Proc. of the 9th ACM Conf. on Recommender Systems. New York: ACM, 2015. 301−304. [doi: 10.1145/2792838.
2799679]
[14] Cheng C, Yang H, King I, Lyu MR. Fused matrix factorization with geographical and social influence in location-based social
networks. In: Proc. of the 26th AAAI Conf. on Artificial Intelligence. AAAI, 2012. 17−23.
[15] Zhang C, Wang K. POI recommendation through cross-region collaborative filtering. Knowledge and Information Systems, 2016,
46(2):369−387. [doi: 10.1007/s10115-015-0825-8]
[16] Zheng HT, Zhou Y, Liang N, Xiao X, Sangaiah AK, Zhao C. Exploiting user mobility for time-aware POI recommendation in
social networks. IEEE ACCESS, 2017,PP(99):1. [doi: 10.1109/ACCESS.2017.2764074]
[17] Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM. Time-Aware point-of-interest recommendation. In: Proc. of the 36th Int’l ACM
SIGIR Conf. on Research and Development in Information Retrieval. New York: ACM, 2013. 363−372. [doi: 10.1145/2484028.
2484030]