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Haseeb Rehman Khan
Faculty of Art, Computing & Creative Industry, Sultan Idris Education University, Tanjong Malim 35900, Perak, Malaysia

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Journal article
Published: 21 July 2021 in Sustainability
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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

ACS Style

Haseeb Rehman Khan; Chen Lim; Minhaz Ahmed; Kian Tan; Mazlin Bin Mokhtar. Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism. Sustainability 2021, 13, 8141 .

AMA Style

Haseeb Rehman Khan, Chen Lim, Minhaz Ahmed, Kian Tan, Mazlin Bin Mokhtar. Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism. Sustainability. 2021; 13 (15):8141.

Chicago/Turabian Style

Haseeb Rehman Khan; Chen Lim; Minhaz Ahmed; Kian Tan; Mazlin Bin Mokhtar. 2021. "Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism." Sustainability 13, no. 15: 8141.

Conference paper
Published: 27 September 2018 in AIP Conference Proceedings
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Contextual suggestion systems have been emerging as an entrancing region of research, attributable to the innovative advances in smart connecting things and rapid growth of Big Data. In this regard, the primary purpose of contextual suggestion systems is to propose things that assist users to settle on choices from countless activities, for example, according to their specific context, system may predict that what place users would find interesting to visit or on what restaurant they would prefer to eat. In a smart environment using big data, users’ current activity and past behavior could be incorporated into the suggestion process with an end goal is to provide right suggestion at the right time with appropriate location on users personal preferences. The objective of this paper is to provide an overview of contextual suggestion system and a review of TREC’s contextual suggestion track to investigate the approaches have been used in order to develop a model for contextual suggestion.

ACS Style

Kian Lam Tan; Haseeb Ur Rehman Khan; Chen Kim Lim. Challenges in recommending venues by using contextual suggestion track. AIP Conference Proceedings 2018, 2016, 020143 .

AMA Style

Kian Lam Tan, Haseeb Ur Rehman Khan, Chen Kim Lim. Challenges in recommending venues by using contextual suggestion track. AIP Conference Proceedings. 2018; 2016 (1):020143.

Chicago/Turabian Style

Kian Lam Tan; Haseeb Ur Rehman Khan; Chen Kim Lim. 2018. "Challenges in recommending venues by using contextual suggestion track." AIP Conference Proceedings 2016, no. 1: 020143.