Tourism Recommendation System Based on User Reviews

Alnogaithan, Omamah; Algazlan, Sumaiah; Aljuraiban, Aljoharah; Shargabi, Amal

doi:10.1109/3ict.2019.8910312

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The authors of [142][143][144][145][146] proposed a context-aware recommendation system using smartphone sensors integrated with smart city applications and e-tourism, another recommendation system based on tourist context [147,148]. The authors of [49,73,149,150] proposed a system of travel recommendations that mines appropriate locations, context, user preferences [151][152][153] users' reviews [154], sentiments analysis [1], and users' physical and psychological functionality levels [155].…”

Section: Traveling and Poimentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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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.

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Section: Traveling and Poimentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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“…The solution for choosing tourist attractions can be by using a recommendation system technology that is able to recommend tourist attractions in an area [2]. Various approaches and types of recommendation systems have been applied by many people in the tourism sector such as the collaborative filtering method [3], content-based filtering [4], demographic filtering [5], and several other approaches. The most frequently used approaches are collaborative filtering and content-based filtering.…”

Section: Introductionmentioning

confidence: 99%

“…L. Gang [3] built a system for recommending tourist attractions using a collaborative filtering approach. Meanwhile, O. Alnogaithan also implemented content-based filtering by utilizing user reviews [4]. Demographic filtering was also applied by Y. Wang [5] in building a tourist spot recommendation system by applying several machine learning methods such as naïve bayes, bayesian network, and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Recommender System Using Singular Value Decomposition and Support Vector Machine in Bali Tourism

Pramarta¹,

Baizal

2022

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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When going to make a visit to a tourist area, tourists must determine the place they want to visit. Meanwhile, the desired place has several categories and types. The many types of tourist attractions make tourists confused in determining their choice. Therefore, we focus on developing a hybrid recommendation system by combining several recommendations approaches, namely collaborative filtering, content-based filtering, and demographic filtering. This recommendation system was built to solve the cold start problem that often appears in collaborative filtering and content-based filtering. In this study, weighted and switching techniques were chosen as the hybridization method. These two techniques are used to overcome the weaknesses of each technique so that it becomes a better recommendation system. The singular value decomposition (SVD) algorithm was chosen to be used in collaborative filtering, meanwhile, content-based filtering uses the calculation of cosine similarity values , and demographic filtering uses the support vector machine (SVM) algorithm. The data used in this study is data on tourist destinations in the Bali area obtained from crawling on the TripAdvisor site. In this study, the root mean square error (RMSE) and mean absolute error (MAE) was used to measure the accuracy of the resulting rating prediction. The results of the experiments carried out show that the hybrid method that was built produces better accuracy prediction results than when run separately with an average mean absolute error (MAE) of 0.6660 and a root mean square error (RMSE) of 0.8644.

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Vacation Recommendation System Using New York Times Articles

Trivedi,

Sivagami

2024

Communications in Computer and Information Science

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Tourism Recommendation System Based on User Reviews

Cited by 3 publications

References 15 publications

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

Hybrid Recommender System Using Singular Value Decomposition and Support Vector Machine in Bali Tourism

Vacation Recommendation System Using New York Times Articles

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