Attentional Memory Network with Correlation-based Embedding for time-aware POI recommendation

Knowledge-Based Systems - Tập 214 - Trang 106747 - 2021
Meihui Shi1, Derong Shen1, Yue Kou1, Tiezheng Nie1, Ge Yu1
1College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

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