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Hamari, M. Sj\u00f6klint, and A. Ukkonen, \u201cThe sharing economy: Why people participate in collaborative consumption,\u201d Journal of the Association for Information Science and Technology, vol.67, no.9, pp.2047-2059, 2016. 10.1002\/asi.23552","DOI":"10.1002\/asi.23552"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] G. Santos, M. Santos, V.F.S. Mota, F. Benevenuto, and T.H. Silva, \u201cNeutral or negative? Sentiment evaluation in reviews of hosting services,\u201d Proc. 24th Brazilian Symposium on Multimedia and the Web, pp.347-354, 2018. 10.1145\/3243082.3243091","DOI":"10.1145\/3243082.3243091"},{"key":"3","unstructured":"[3] S. Heikkil\u00e4, \u201cMobility as a service \u2014 A proposal for action for the public administration, case Helsinki,\u201d Master&apos;s Thesis, Aalto University, School of Engineering, 2014."},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] E. Bothos, B. Magoutas, K. Arnaoutaki, and G. Mentzas, \u201cLeveraging blockchain for open mobility-as-a-service ecosystems,\u201d IEEE\/WIC\/ACM International Conference on Web Intelligence-Companion Volume, WI&apos;19 Companion, New York, NY, USA, pp.292-296, ACM, 2019. 10.1145\/3358695.3361844","DOI":"10.1145\/3358695.3361844"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] R. Christiaanse, \u201cMobility as a service,\u201d Companion Proceedings of the 2019 World Wide Web Conference, WWW&apos;19, New York, NY, USA, pp.83-92, ACM, 2019. 10.1145\/3308560.3317050","DOI":"10.1145\/3308560.3317050"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] P. Georgakis, A. Almohammad, E. Bothos, B. Magoutas, K. Arnaoutaki, and G. Mentzas, \u201cMultimodal route planning in mobility as a service,\u201d IEEE\/WIC\/ACM International Conference on Web Intelligence-Companion Volume, WI&apos;19 Companion, New York, NY, USA, pp.283-291, ACM, 2019. 10.1145\/3358695.3361843","DOI":"10.1145\/3358695.3361843"},{"key":"7","unstructured":"[7] D.A. Hensher, C.Q. Ho, C. Mulley, J.D. Nelson, G. Smith, and Y.Z. Wong, Understanding Mobility as a Service (MaaS): Past, present and future, Elsevier, 2020. 10.1016\/C2019-0-00508-0"},{"key":"8","unstructured":"[8] N.H. Liu, S.. Lai, C. Chen, and S.J. Hsieh, \u201cAdaptive music recommendation based on user behavior in time slot,\u201d International Journal of Computer Science and Network Security, vol.9, no.2, pp.219-227, 2009."},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] J.M. Noguera, M.J. Barranco, R.J. Segura, and L. Mart\u00ednez, \u201cA mobile 3D-GIS hybrid recommender system for tourism,\u201d Information Sciences, vol.215, pp.37-52, 2012. 10.1016\/j.ins.2012.05.010","DOI":"10.1016\/j.ins.2012.05.010"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] S.-Y. Hwang and W.-S. Yang, \u201cOn-tour attraction recommendation in a mobile environment,\u201d 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, pp.661-666, IEEE, 2012. 10.1109\/percomw.2012.6197597","DOI":"10.1109\/PerComW.2012.6197597"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] D. Gavalas, C. Konstantopoulos, K. Mastakas, and G. Pantziou, \u201cMobile recommender systems in tourism,\u201d Journal of Network and Computer Applications, vol.39, pp.319-333, 2014. 10.1016\/j.jnca.2013.04.006","DOI":"10.1016\/j.jnca.2013.04.006"},{"key":"12","unstructured":"[12] Y. Zhao, J. Zhao, L. Jiang, R. Tan, and D. Niyato, \u201cMobile edge computing, blockchain and reputation-based crowdsourcing IoT federated learning: A secure, decentralized and privacy-preserving system,\u201d arXiv preprint arXiv:1906.10893, 2019."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] D. Li, R. Rzepka, M. Ptaszynski, and K. Araki, \u201cHEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media,\u201d Information Processing &amp; Management, vol.57, no.6, 102290, 2020. 10.1016\/j.ipm.2020.102290","DOI":"10.1016\/j.ipm.2020.102290"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] Y. Kanza, E. Kravi, E. Safra, and Y. Sagiv, \u201cLocation-based distance measures for geosocial similarity,\u201d ACM Trans. Web (TWEB), vol.11, no.3, pp.1-32, 2017. 10.1145\/3054951","DOI":"10.1145\/3054951"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] J. Kim, J.-G. Lee, B.S. Lee, and J. Liu, \u201cGeosocial co-clustering: A novel framework for geosocial community detection,\u201d ACM Trans. Intell. Syst. Technol. (TIST), vol.11, no.4, pp.1-26, 2020. 10.1145\/3391708","DOI":"10.1145\/3391708"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] D. Chandrasekaran and V. Mago, \u201cEvolution of semantic similarity \u2014 A survey,\u201d ACM Computing Surveys (CSUR), vol.54, no.2, pp.1-37, 2022. 10.1145\/3440755","DOI":"10.1145\/3440755"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] J.-B. Gao, B.-W. Zhang, and X.-H. Chen, \u201cA WordNet-based semantic similarity measurement combining edge-counting and information content theory,\u201d Engineering Applications of Artificial Intelligence, vol.39, pp.80-88, 2015. 10.1016\/j.engappai.2014.11.009","DOI":"10.1016\/j.engappai.2014.11.009"},{"key":"18","unstructured":"[18] Z. Wang, H. Mi, and A. 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Wenk, \u201cA path-based distance for street map comparison,\u201d ACM Trans. Spat. Algorithms Syst. (TSAS), vol.1, no.1, pp.1-28, 2015. 10.1145\/2729977","DOI":"10.1145\/2729977"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] P. Kiefer and I. Giannopoulos, \u201cGaze map matching: Mapping eye tracking data to geographic vector features,\u201d Proc. 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL&apos;12, New York, NY, USA, pp.359-368, Association for Computing Machinery, 2012. 10.1145\/2424321.2424367","DOI":"10.1145\/2424321.2424367"},{"key":"30","doi-asserted-by":"crossref","unstructured":"[30] R. Aydo\u011fan and P. Yolum, \u201cLearning consumer preferences using semantic similarity,\u201d Proc. 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS&apos;07, New York, NY, USA, Association for Computing Machinery, 2007. 10.1145\/1329125.1329401","DOI":"10.1145\/1329125.1329401"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] S.M.S. J\u00fanior and M.G. Manzato, \u201cCollaborative filtering based on semantic distance among items,\u201d Proc. 21st Brazilian Symposium on Multimedia and the Web, WebMedia&apos;15, New York, NY, USA, pp.53-56, Association for Computing Machinery, 2015. 10.1145\/2820426.2820466","DOI":"10.1145\/2820426.2820466"},{"key":"32","doi-asserted-by":"crossref","unstructured":"[32] G. Piao and J.G. 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Hirst, \u201cDistributional measures of concept-distance: A task-oriented evaluation,\u201d Proc. 2006 Conference on Empirical Methods in Natural Language Processing, pp.35-43, 2006. 10.3115\/1610075.1610081","DOI":"10.3115\/1610075.1610081"},{"key":"36","unstructured":"[36] S. Mohammad, I. Gurevych, G. Hirst, and T. Zesch, \u201cCross-lingual distributional profiles of concepts for measuring semantic distance,\u201d Proc. 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp.571-580, 2007."},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] K. Potdar, T.S. Pardawala, and C.D. Pai, \u201cA comparative study of categorical variable encoding techniques for neural network classifiers,\u201d International Journal of Computer Applications, vol.175, no.4, pp.7-9, 2017. 10.5120\/ijca2017915495","DOI":"10.5120\/ijca2017915495"},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] J. 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