@inproceedings{Solomon2018,
author = {Solomon, Adir and Bar, Ariel and Yanai, Chen and Shapira, Bracha and Rokach, Lior},
title = {Predict Demographic Information Using Word2vec on Spatial Trajectories},
year = {2018},
isbn = {9781450355896},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209219.3209224},
doi = {10.1145/3209219.3209224},
booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization},
pages = {331–339},
numpages = {9},
keywords = {Word2vec, trajectories, embedding, deep learning},
location = {Singapore, Singapore},
series = {UMAP '18}
}

@inproceedings{Liu2016,
author = {Liu, Xin and Liu, Yong and Li, Xiaoli},
title = {Exploring the Context of Locations for Personalized Location Recommendations},
year = {2016},
isbn = {9781577357704},
publisher = {AAAI Press},
abstract = {Conventional location recommendation models rely on users' visit history, geographical influence, temporal influence, etc., to infer users' preferences for locations. However, systematically modeling a location's context (i.e., the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skipgram model, we learn the latent representation for a location to capture the influence of its context. A pair-wise ranking loss that considers the confidences of observed user preferences for locations is then proposed to learn users' latent representations for personalized top- N location recommendations. Moreover, we also extend our model by taking into account temporal influence. Stochastic gradient descent based optimization algorithms are developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location recommendation methods.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence},
pages = {1188--1194},
numpages = {7},
location = {New York, New York, USA},
series = {IJCAI'16},
url       = {http://www.ijcai.org/Abstract/16/172},
}




@inproceedings{Feng2017,
  author    = {Shanshan Feng and
               Gao Cong and
               Bo An and
               Yeow Meng Chee},
  title     = {{POI}2{V}ec: Geographical Latent Representation for Predicting Future
               Visitors},
  booktitle = {Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence,
               February 4-9, 2017, San Francisco, California, {USA.}},
  pages     = {102--108},
  year      = {2017},
  url       = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14902},
  timestamp = {Mon, 06 Mar 2017 11:36:24 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/aaai/FengCAC17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@incollection{MikolovSCCD13,
title = {Distributed Representations of Words and Phrases and their Compositionality},
author = {Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg S and Dean, Jeff},
booktitle = {Advances in Neural Information Processing Systems 26},
editor = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
pages = {3111--3119},
year = {2013},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf}
}


@inproceedings{
AdiKBLG17,
title={Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks},
author={Yossi Adi and
               Einat Kermany and
               Yonatan Belinkov and
               Ofer Lavi and
               Yoav Goldberg},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=BJh6Ztuxl},
}

@article{Zhu2019,
  author={M. {Zhu} and W. {Chen} and J. {Xia} and Y. {Ma} and Y. {Zhang} and Y. {Luo} and Z. {Huang} and L. {Liu}},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  title={Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations},
  year={2019},
  volume={20},
  number={10},
  pages={3981--3990},
  doi={10.1109/TITS.2019.2901117},
}

@article{kroenke2001phq,
  title={The {PHQ}-9: validity of a brief depression severity measure},
  author={Kroenke, Kurt and Spitzer, Robert L and Williams, Janet BW},
  journal={Journal of {G}eneral {I}nternal {M}edicine},
  volume={16},
  number={9},
  pages={606--613},
  year={2001},
  doi={10.1046/j.1525-1497.2001.016009606.x},
  publisher={Wiley Online Library}
}

@article{salton1988term,
author = {Salton, Gerard and Buckley, Christopher},
journal = {Information processing {\&} management},
mendeley-groups = {Research},
number = {5},
pages = {513--523},
publisher = {Elsevier},
title = {{Term-weighting approaches in automatic text retrieval}},
volume = {24},
doi={10.1016/0306-4573(88)90021-0},
year = {1988}
}

@inproceedings{Ye2019,
author = {Ye, Junting and Skiena, Steven},
title = {The Secret Lives of Names?: Name Embeddings from Social Media},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3330751},
doi = {10.1145/3292500.3330751},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages = {3000--3008},
numpages = {9},
keywords = {lifespan modeling, communication homophily, twitter name embeddings},
location = {Anchorage, AK, USA},
series = {KDD '19}
}

@inproceedings{chang2018,
  title     = {Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation},
  author    = {Buru Chang and Yonggyu Park and Donghyeon Park and Seongsoon Kim and Jaewoo Kang},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on
               Artificial Intelligence, {IJCAI-18}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {3301--3307},
  year      = {2018},
  month     = {7},
  doi       = {10.24963/ijcai.2018/458},
  url       = {https://doi.org/10.24963/ijcai.2018/458},
}

@article{crivellari2019motion,
  title={From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data},
  author={Crivellari, Alessandro and Beinat, Euro},
  journal={ISPRS International Journal of Geo-Information},
  doi = {10.3390/ijgi8030134},
  volume={8},
  number={3},
  pages={134},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}
