Self-supervised representation learning for trip recommendation

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: Proceedings of the Web Conference 2020, 2020, pp. 1264–1274. Shi, 2021, Attentional memory network with correlation-based embedding for time-aware POI recommendation, Knowl.-Based Syst., 214, 10.1016/j.knosys.2021.106747 Y. Wu, D. Lian, S. Jin, E. Chen, Graph convolutional networks on user mobility heterogeneous graphs for social relationship inference, in: IJCAI, 2019, pp. 3898–3904. Zhou, 2021, Self-supervised human mobility learning for next location prediction and trajectory classification, Knowl.-Based Syst., 10.1016/j.knosys.2021.107214 Chen, 2021, A multi-task learning approach for improving travel recommendation with keywords generation, Knowl.-Based Syst., 10.1016/j.knosys.2021.107521 He, 2019, A joint context-aware embedding for trip recommendations, 292 Wang, 2021, Personalized route recommendation with neural network enhanced A* search algorithm, IEEE Trans. Knowl. Data Eng. K.H. Lim, J. Chan, C. Leckie, S. Karunasekera, Personalized tour recommendation based on user interests and points of interest visit durations, in: IJCAI, 2015, pp. 1778–1784. D. Chen, C.S. Ong, L. Xie, Learning points and routes to recommend trajectories, in: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016, pp. 2227–2232. K. Taylor, K.H. Lim, J. Chan, Travel itinerary recommendations with must-see points-of-interest, in: Companion Proceedings of the the Web Conference 2018, 2018, pp. 1198–1205. N.L. Ho, K.H. Lim, User Preferential Tour Recommendation Based on POI-Embedding Methods, in: 26th International Conference on Intelligent User Interfaces, 2021, pp. 46–48. Zhou, 2020, Semi-supervised trajectory understanding with POI attention for end-to-end trip recommendation, ACM Trans. Spatial Algorithms Syst. (TSAS), 6, 1, 10.1145/3378890 Gao, 2021, Adversarial human trajectory learning for trip recommendation, IEEE Trans. Neural Netw. Learn. Syst., 10.1109/TNNLS.2021.3058102 Liu, 2021, Self-supervised learning: Generative or contrastive, IEEE Trans. Knowl. Data Eng., 10.1109/TKDE.2021.3090866 Ohri, 2021, Review on self-supervised image recognition using deep neural networks, Knowl.-Based Syst., 224, 10.1016/j.knosys.2021.107090 J. Gu, C. Song, W. Jiang, X. Wang, M. Liu, Enhancing personalized trip recommendation with attractive routes, in: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, pp. 662–669. V. Rakesh, N. Jadhav, A. Kotov, C.K. Reddy, Probabilistic social sequential model for tour recommendation, in: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017, pp. 631–640. Jiang, 2021 Mikolov, 2013 Q. Gao, F. Zhou, K. Zhang, G. Trajcevski, X. Luo, F. Zhang, Identifying human mobility via trajectory embeddings, in: IJCAI, 2017, pp. 1689–1695. J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, X. Xie, Self-supervised graph learning for recommendation, in: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 726–735. Che, 2021, Multi-aspect self-supervised learning for heterogeneous information network, Knowl.-Based Syst., 10.1016/j.knosys.2021.107474 Liu, 2020, Online anomalous trajectory detection with deep generative sequence modeling, 949 P. Wang, Y. Fu, H. Xiong, X. Li, Adversarial substructured representation learning for mobile user profiling, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 130–138. K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738. Oord, 2018 Z. Xie, C. Liu, Y. Zhang, H. Lu, D. Wang, Y. Ding, Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation, in: Proceedings of the Web Conference 2021, 2021, pp. 449–459. Zhao, 2020, Self-supervised representation learning by predicting visual permutations, Knowl.-Based Syst., 210, 10.1016/j.knosys.2020.106534 E. Xie, J. Ding, W. Wang, X. Zhan, H. Xu, P. Sun, Z. Li, P. Luo, Detco: Unsupervised contrastive learning for object detection, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 8392–8401. A. Aberdam, R. Litman, S. Tsiper, O. Anschel, R. Slossberg, S. Mazor, R. Manmatha, P. Perona, Sequence-to-sequence contrastive learning for text recognition, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15302–15312. Yan, 2021 Aitchison, 2021 K. Zhou, H. Wang, W.X. Zhao, Y. Zhu, S. Wang, F. Zhang, Z. Wang, J.-R. Wen, S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization, in: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, pp. 1893–1902. Hinton, 2012 F. Zhou, X. Yue, G. Trajcevski, T. Zhong, K. Zhang, Context-aware variational trajectory encoding and human mobility inference, in: The World Wide Web Conference, 2019, pp. 3469–3475. Duan, 2020, Personalized tourism route recommendation based on user’s active interests, 729 Kingma, 2014 B. Perozzi, R. Al-Rfou, S. Skiena, Deepwalk: Online learning of social representations, in: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 701–710. Kipf, 2016 J.N. Van Rijn, F. Hutter, Hyperparameter importance across datasets, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 2367–2376. Altmann, 2010, Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340, 10.1093/bioinformatics/btq134