Abstract
With the deployment of information and communication technologies (ICTs) and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning (CBR) as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions.
Highlights
Research and development in smart city have emerged as a response to mitigate the issue of rapid urbanization
This study is concerned with information retrieval, formal and quantitative evaluation of precision and relevance using known metrics such as normalized distance-based performance measure (NDPM), normalized discounted cumulative gain (NDCG), average distance measure (ADM), mean average precision (MAP), etc. was not employed, since this study is not employing experiments to evaluate the effectiveness of web search engine retrieval algorithms
An unbalanced scale was employed as it helps to measure the perception of the respondents in relation to the applicability of the case-based reasoning (CBR) recommender system without influencing their judgement in relation to the questionnaire items as compared to using a balance scale which may lead to bias of response from the respondents
Summary
Research and development in smart city have emerged as a response to mitigate the issue of rapid urbanization. While at the same time improving resource consumption (Anthony Jnr et al 2020) In this sense, smart cities aim to use innovative ICT solutions to manage urban challenges related to environment, people, mobility, security, economy, resource management, public health, etc. Smart city uses ICT as a prime enabler to improve integration of data to be transformed into useful information and knowledge intelligence for the sustainability of cities (Jnr et al 2018). Recommender systems utilize data mining techniques in providing suggestions based on aggregated data It entails a description of user keywords that is either matched in the data catalog (Abu-Issa et al 2017). Recommender systems have been deployed in many fields such as data warehouse, information retrieval, e-commerce, cognitive science, web usage mining, and many others (Negre and Rosenthal-Sabroux 2014)
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