Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation
Tài liệu tham khảo
X. Zhou, C. Mascolo, Z. Zhao, Topic-enhanced memory networks for personalised point-of-interest recommendation, in: ACM SIGKDD, 2019, pp. 3018–3028.
Q. Liu, S. Wu, L. Wang, T. Tan, Predicting the next location: A recurrent model with spatial and temporal contexts, in: AAAI, 2016, pp. 194–200.
Q. Yuan, G. Cong, Z. Ma, A. Sun, N. Magnenat-Thalmann, Time-aware point-of-interest recommendation, in: ACM SIGIR, 2013, pp. 363–372.
X. Li, G. Cong, X. Li, T.N. Pham, S. Krishnaswamy, Rank-GeoFM: A Ranking based geographical factorization method for point of interest recommendation, in: ACM SIGIR, 2015, pp. 433–442.
Y. Si, F. Zhang, W. Liu, A Time-aware POI recommendation method exploiting user-based collaborative filtering and location popularity, in: DEStech Transactions on Computer Science and Engineering, 2017.
P. Zhao, H. Zhu, Y. Liu, J. Xu, Z. Li, F. Zhuang, V.S. Sheng, X. Zhou, Where to go next: A spatio-temporal gated network for next POI recommendation, in: AAAI, 2019, pp. 5877–5884.
N. Lim, B. Hooi, S. Ng, X. Wang, Y.L. Goh, R. Weng, J. Varadarajan, STP-UDGAT: Spatial-temporal-preference user dimensional graph attention network for next POI recommendation, in: CIKM, 2020, pp. 845–854.
R. Li, Y. Shen, Y. Zhu, Next point-of-interest recommendation with temporal and multi-level context attention, in: ICDM, 2018, pp. 1110–1115.
D. Kong, F. Wu, HST-LSTM: A hierarchical spatial-temporal long-short term memory network for location prediction, in: IJCAI, 2018, pp. 2341–2347.
Zheng, 2017, Exploiting user mobility for time-aware POI recommendation in social networks, IEEE Access, 1, 10.1109/ACCESS.2017.2764074
W. Liu, Z. Wang, B. Yao, J. Yin, Geo-ALM: POI recommendation by fusing geographical information and adversarial learning mechanism, in: IJCAI, 2019, pp. 1807–1813.
B. Chang, Y. Park, D. Park, S. Kim, J. Kang, Content-aware hierarchical point-of-interest embedding model for successive POI recommendation, in: IJCAI, 2018, pp. 3301–3307.
Angulo, 2020, Bridging cognitive models and recommender systems, Cogn. Comput., 12, 426, 10.1007/s12559-020-09719-3
Contreras, 2020, A cognitively inspired clustering approach for critique-based recommenders, Cogn. Comput., 12, 428, 10.1007/s12559-018-9586-5
Zhao, 2019, Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems, Knowl. Based Syst., 166, 132, 10.1016/j.knosys.2018.12.022
M. Ye, P. Yin, W. Lee, D.L. Lee, Exploiting geographical influence for collaborative point-of-interest recommendation, in: ACM SIGIR, 2011, pp. 325–334.
X. Liu, K. Aberer, SoCo: a social network aided context-aware recommender system, in: WWW, 2013, pp. 781–802.
X. Liu, Y. Liu, K. Aberer, C. Miao, Personalized point-of-interest recommendation by mining users’ preference transition, in: CIKM, 2013, pp. 733–738.
Liu, 2020, Mix geographical information into local collaborative ranking for POI recommendation, World Wide Web, 23, 131, 10.1007/s11280-019-00681-1
Si, 2019, An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features, Knowl. Based Syst., 163, 267, 10.1016/j.knosys.2018.08.031
J. Zhang, C. Chow, GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations, in: SIGIR, 2015, pp. 443–452.
C. Cheng, H. Yang, I. King, M.R. Lyu, Fused matrix factorization with geographical and social influence in location-based social networks, in: AAAI, 2012.
C. Cheng, H. Yang, M.R. Lyu, I. King, Where you like to go next: Successive point-of-interest recommendation, in: IJCAI, 2013, pp. 2605–2611.
S. Feng, X. Li, Y. Zeng, G. Cong, Y.M. Chee, Q. Yuan, Personalized ranking metric embedding for next new POI recommendation, in: IJCAI, 2015, pp. 2069–2075.
K. Sun, T. Qian, T. Chen, Y. Liang, Q.V.H. Nguyen, H. Yin, Where to go next: Modeling long- and short-term user preferences for point-of-interest recommendation, in: AAAI, 2020, pp. 214–221.
F. Yu, L. Cui, W. Guo, X. Lu, Q. Li, H. Lu, A category-aware deep model for successive POI recommendation on sparse check-in data, in: WWW, 2020, pp. 1264–1274.
Liu, 2015, A general geographical probabilistic factor model for point of interest recommendation, IEEE Trans. Knowl. Data Eng., 27, 1167, 10.1109/TKDE.2014.2362525
Li, 2017, A time-aware personalized point-of-interest recommendation via high-order tensor factorization, ACM Trans. Inf. Syst., 35, 31:1, 10.1145/3057283
Ding, 2018, Spatial-temporal distance metric embedding for time-specific POI recommendation, IEEE Access, 6, 67035, 10.1109/ACCESS.2018.2869994
Q. Yuan, G. Cong, A. Sun, Graph-based point-of-interest recommendation with geographical and temporal influences, in: CIKM, 2014, pp. 659–668.
Gao, 2020, Exploiting location-based context for POI recommendation when traveling to a new region, IEEE Access, 8, 52404, 10.1109/ACCESS.2020.2980982
Cambria, 2016, Affective computing and sentiment analysis, IEEE Intell. Syst., 31, 102, 10.1109/MIS.2016.31
Li, 2019, Learning binary codes with neural collaborative filtering for efficient recommendation systems, Knowl. Based Syst., 172, 64, 10.1016/j.knosys.2019.02.012
T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, in: ICLR, 2013.
T. Luong, H. Pham, C.D. Manning, Effective approaches to attention-based neural machine translation, in: EMNLP, 2015, pp. 1412–1421.
K. Xu, J. Ba, R. Kiros, K. Cho, A.C. Courville, R. Salakhutdinov, R.S. Zemel, Y. Bengio, Show, attend and tell: Neural image caption generation with visual attention, in: ICML, Vol. 37, 2015, pp. 2048–2057.
S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, in: UAI, 2009, pp. 452–461.
D. Xi, F. Zhuang, Y. Liu, J. Gu, H. Xiong, Q. He, Modelling of bi-directional spatio-temporal dependence and users’ dynamic preferences for missing POI check-in identification, in: AAAI, 2019, pp. 5458–5465.
Qian, 2019, Spatiotemporal representation learning for translation-based POI recommendation, ACM Trans. Inf. Syst., 37, 18:1, 10.1145/3295499