Abstract
The concept of “smart city” has not yet been clearly defined. However, there are six characteristics/categories for classifying this kind of cities and compare them: smart economy , smart mobility, smart environment, smart people , smart living and smart governance. However, being “smart” is a challenge increasingly important for many cities or communities. This is of particular interest in the domain of Information and Communications Technology (ICT) and for such systems where there are economic, social, and other issues. To the best of our knowledge, there are no studies that attempt to help identifying the actions to be implemented to improve the smartness of a city. Recommending such actions is an emerging and promising field of investigation. Usually, recommender systems try to predict the rating that a user would give to an item (such as music, books, …) he has not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user’s social environment (collaborative filtering approaches). In this chapter, we present a framework for a recommender system for cities. The scope of this research work is to take advantage from recognized “smart cities” and to make same actions for city who wants to become “smart”. The followed method is: having a list of characteristics of a “smart city”, and having a city which wants to become “smart”, which actions must be implemented to become “smart” regarding the characteristics of “smartness”. This framework uses the actions already implemented in smart cities to enhance the smartness of a given city. The main idea is to recommend to the city the actions already implemented in those smart cities that are similar (the similarity between two cities is based on some indicators such as air quality, water consumption, etc.) as the actions to be implemented in the said city. This is done by (1) Pre-treating the indicators values of a given smart city category (only one among the six), (2) Matching the indicators corresponding to this category, (3) Returning to the city the actions to be implemented in a given order (according to the preferences of the city which needs help, for example). Thus, the city will be able to improve its smartness.
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Notes
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- 2.
Ratings/scores are given by a person authorized to make the decision of implementing different actions and through this score indicates whether the action is (or was) relevant.
- 3.
The log may be a database or other data structure. It will be powered by the recommender system as and when the use of the system by cities. However, for the initial data (the so-called cold start problem), we hope to use the data of the official, public and freely sources, possibly enriched with the participation of volunteers.
References
Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.
Baeza-Yates, R., Hurtado, C., Mendoza, M., Dupret, G. (2005). Modeling user search behavior. LA-WEB ‘05: Proceedings of the third Latin American Web Congress, (p. 242). IEEE Computer Society, Washington, DC, USA.
Baeza-Yates, R.A., Hurtado, C.A., Mendoza M. (2004). Query recommendation using query logs in search engines. In W. Lindner, M. Mesiti, C. Türker, Y. Tzitzikas & A. Vakali (Eds), EDBT Workshops, volume 3268 of Lecture Notes in Computer Science (pp. 588–596). Springer.
Baeza-Yates, A. R., & Berthier, B. A. (1999). Modern Information Retrieval. New York: ACM Press.
Bettencourt, L., & West, G. (2010). A unified theory of urban living. Nature, 467(7318), 912–913.
Bettencourt, L. M. A., Lobo, J., Strumsky, D., & West, G. B. (2010). Urban scaling and its deviations: Revealing the structure of wealth, innovation and crime across cities. PLoS ONE, 5(11), e13541.
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J., Mellouli, S., Nahon, K., Pardo,T.A.,& Scholl, H.J.(2012). Understanding smart cities: An integrative framework. In Proceedings of the 2012 45th Hawaii International Conference on System Sciences, HICSS ‘12 (pp. 2289–2297). IEEE Computer Society, Washington, DC, USA.
Fu Y., & Shih, M-Y. (2002). A Framework for Personal Web Usage Mining. In International Conference on Internet Computing, pp. 595–600.
Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic’, N., & Meijers, E. (2007). Smart cities: Ranking of European medium-sized cities. Centre of Regional Science SRF: Vienna University of Technology.
IBM. Smarter cities. http://smartcitieschallenge.org, 2013.
Kent, A. (1971). Information analysis and retrieval. Wiley, Inc.
Larousse. Action. http://www.larousse.fr/dictionnaires/francais/action/924, 2013.
Marcel, P., Negre, E. (2011) A survey of query recommendation techniques for datawarehouse exploration. In EDA.
Pierrakos, D., Paliouras, G., Papatheodorou, C., & Spyropoulos, C. D. (2003). Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction, 13(4), 311–372.
Salton, G.(1983). Introduction to modern information retrieval (McGraw-Hill Computer Science Series). New York: McGraw.
Schafer, J. B., Joseph Konstan, A., & Riedl, John. (2001). e-Commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2), 115–153.
Schaffers, H., Komninos, N., & Pallot, M. (2012). Smart cities as innovation ecosystems sustained by the future internet. Technical report.
Srivastava, J., Cooley, R., Deshpande, M., & Tan, Pang-Ning. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 12–23.
The Committee of Digital and Knowledge-based Cities (CDC) of United Cities and Local Governments (UCLG). Smart cities study: International study on the situation of ict, innovation and knowledge in cities. Chaired by Inaki Azkuna, Major of the City of Bilabo, 2013.
White, R.W., Bilenko, M., & Cucerzan, S. (2007). Studying the use of popular destinations to enhance web search interaction. In SIGIR, (pp. 159–166).
Wiktionary. Action. http://en.wiktionary.org/wiki/action, 2013.
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Negre, E., Rosenthal-Sabroux, C. (2014). Recommendations to Improve the Smartness of a City. In: Dameri, R., Rosenthal-Sabroux, C. (eds) Smart City. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-06160-3_5
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